Regression

class pycaret.regression.RegressionExperiment

setup(data: Optional[Union[dict, list, tuple, ndarray, spmatrix, DataFrame]] = None, data_func: Optional[Callable[[], Union[dict, list, tuple, ndarray, spmatrix, DataFrame]]] = None, target: Union[int, str, list, tuple, ndarray, Series] = -1, index: Union[bool, int, str, list, tuple, ndarray, Series] = True, train_size: float = 0.7, test_data: Optional[Union[dict, list, tuple, ndarray, spmatrix, DataFrame]] = None, ordinal_features: Optional[Dict[str, list]] = None, numeric_features: Optional[List[str]] = None, categorical_features: Optional[List[str]] = None, date_features: Optional[List[str]] = None, text_features: Optional[List[str]] = None, ignore_features: Optional[List[str]] = None, keep_features: Optional[List[str]] = None, preprocess: bool = True, create_date_columns: List[str] = ['day', 'month', 'year'], imputation_type: Optional[str] = 'simple', numeric_imputation: str = 'mean', categorical_imputation: str = 'mode', iterative_imputation_iters: int = 5, numeric_iterative_imputer: Union[str, Any] = 'lightgbm', categorical_iterative_imputer: Union[str, Any] = 'lightgbm', text_features_method: str = 'tf-idf', max_encoding_ohe: int = 25, encoding_method: Optional[Any] = None, rare_to_value: Optional[float] = None, rare_value: str = 'rare', polynomial_features: bool = False, polynomial_degree: int = 2, low_variance_threshold: Optional[float] = None, group_features: Optional[list] = None, group_names: Optional[Union[str, list]] = None, drop_groups: bool = False, remove_multicollinearity: bool = False, multicollinearity_threshold: float = 0.9, bin_numeric_features: Optional[List[str]] = None, remove_outliers: bool = False, outliers_method: str = 'iforest', outliers_threshold: float = 0.05, transformation: bool = False, transformation_method: str = 'yeo-johnson', normalize: bool = False, normalize_method: str = 'zscore', pca: bool = False, pca_method: str = 'linear', pca_components: Optional[Union[int, float, str]] = None, feature_selection: bool = False, feature_selection_method: str = 'classic', feature_selection_estimator: Union[str, Any] = 'lightgbm', n_features_to_select: Union[int, float] = 0.2, transform_target: bool = False, transform_target_method: str = 'yeo-johnson', custom_pipeline: Optional[Any] = None, custom_pipeline_position: int = -1, data_split_shuffle: bool = True, data_split_stratify: Union[bool, List[str]] = False, fold_strategy: Union[str, Any] = 'kfold', fold: int = 10, fold_shuffle: bool = False, fold_groups: Optional[Union[str, DataFrame]] = None, n_jobs: Optional[int] = -1, use_gpu: bool = False, html: bool = True, session_id: Optional[int] = None, system_log: Union[bool, str, Logger] = True, log_experiment: Union[bool, str, BaseLogger, List[Union[str, BaseLogger]]] = False, experiment_name: Optional[str] = None, experiment_custom_tags: Optional[Dict[str, Any]] = None, log_plots: Union[bool, list] = False, log_profile: bool = False, log_data: bool = False, engine: Optional[Dict[str, str]] = None, verbose: bool = True, memory: Union[bool, str, Memory] = True, profile: bool = False, profile_kwargs: Optional[Dict[str, Any]] = None)

This function initializes the training environment and creates the transformation pipeline. Setup function must be called before executing any other function. It takes two mandatory parameters: data and target. All the other parameters are optional.

Example

>>> from pycaret.datasets import get_data
>>> juice = get_data('juice')
>>> from pycaret.classification import *
>>> exp_name = setup(data = juice,  target = 'Purchase')

data: dataframe-like = None

Data set with shape (n_samples, n_features), where n_samples is the number of samples and n_features is the number of features. If data is not a pandas dataframe, it’s converted to one using default column names.

data_func: Callable[[], DATAFRAME_LIKE] = None

The function that generate data (the dataframe-like input). This is useful when the dataset is large, and you need parallel operations such as compare_models. It can avoid broadcasting large dataset from driver to workers. Notice one and only one of data and data_func must be set.

target: int, str or sequence, default = -1

If int or str, respectivcely index or name of the target column in data. The default value selects the last column in the dataset. If sequence, it should have shape (n_samples,). The target can be either binary or multiclass.

index: bool, int, str or sequence, default = True

Handle indices in the data dataframe.

If False: Reset to RangeIndex.
If True: Keep the provided index.
If int: Position of the column to use as index.
If str: Name of the column to use as index.
If sequence: Array with shape=(n_samples,) to use as index.

train_size: float, default = 0.7

Proportion of the dataset to be used for training and validation. Should be between 0.0 and 1.0.

test_data: dataframe-like or None, default = None

If not None, test_data is used as a hold-out set and train_size parameter is ignored. The columns of data and test_data must match.

ordinal_features: dict, default = None

Categorical features to be encoded ordinally. For example, a categorical feature with ‘low’, ‘medium’, ‘high’ values where low < medium < high can be passed as ordinal_features = {‘column_name’ : [‘low’, ‘medium’, ‘high’]}.

numeric_features: list of str, default = None

If the inferred data types are not correct, the numeric_features param can be used to define the data types. It takes a list of strings with column names that are numeric.

categorical_features: list of str, default = None

If the inferred data types are not correct, the categorical_features param can be used to define the data types. It takes a list of strings with column names that are categorical.

date_features: list of str, default = None

If the inferred data types are not correct, the date_features param can be used to overwrite the data types. It takes a list of strings with column names that are DateTime.

text_features: list of str, default = None

Column names that contain a text corpus. If None, no text features are selected.

ignore_features: list of str, default = None

ignore_features param can be used to ignore features during preprocessing and model training. It takes a list of strings with column names that are to be ignored.

keep_features: list of str, default = None

keep_features param can be used to always keep specific features during preprocessing, i.e. these features are never dropped by any kind of feature selection. It takes a list of strings with column names that are to be kept.

preprocess: bool, default = True

When set to False, no transformations are applied except for train_test_split and custom transformations passed in custom_pipeline param. Data must be ready for modeling (no missing values, no dates, categorical data encoding), when preprocess is set to False.

create_date_columns: list of str, default = [“day”, “month”, “year”]

Columns to create from the date features. Note that created features with zero variance (e.g. the feature hour in a column that only contains dates) are ignored. Allowed values are datetime attributes from pandas.Series.dt. The datetime format of the feature is inferred automatically from the first non NaN value.

imputation_type: str or None, default = ‘simple’

The type of imputation to use. Can be either ‘simple’ or ‘iterative’. If None, no imputation of missing values is performed.

numeric_imputation: int, float or str, default = ‘mean’

Imputing strategy for numerical columns. Ignored when imputation_type= iterative. Choose from:

“drop”: Drop rows containing missing values.

“mean”: Impute with mean of column.

“median”: Impute with median of column.

“mode”: Impute with most frequent value.

“knn”: Impute using a K-Nearest Neighbors approach.

int or float: Impute with provided numerical value.

categorical_imputation: str, default = ‘mode’

Imputing strategy for categorical columns. Ignored when imputation_type= iterative. Choose from:

“drop”: Drop rows containing missing values.

“mode”: Impute with most frequent value.

str: Impute with provided string.

iterative_imputation_iters: int, default = 5

Number of iterations. Ignored when imputation_type=simple.

numeric_iterative_imputer: str or sklearn estimator, default = ‘lightgbm’

Regressor for iterative imputation of missing values in numeric features. If None, it uses LGBClassifier. Ignored when imputation_type=simple.

categorical_iterative_imputer: str or sklearn estimator, default = ‘lightgbm’

Regressor for iterative imputation of missing values in categorical features. If None, it uses LGBClassifier. Ignored when imputation_type=simple.

text_features_method: str, default = “tf-idf”

Method with which to embed the text features in the dataset. Choose between “bow” (Bag of Words - CountVectorizer) or “tf-idf” (TfidfVectorizer). Be aware that the sparse matrix output of the transformer is converted internally to its full array. This can cause memory issues for large text embeddings.

max_encoding_ohe: int, default = 25

Categorical columns with max_encoding_ohe or less unique values are encoded using OneHotEncoding. If more, the encoding_method estimator is used. Note that columns with exactly two classes are always encoded ordinally. Set to below 0 to always use OneHotEncoding.

encoding_method: category-encoders estimator, default = None

A category-encoders estimator to encode the categorical columns with more than max_encoding_ohe unique values. If None, category_encoders.target_encoder.TargetEncoder is used.

rare_to_value: float or None, default=None

Minimum fraction of category occurrences in a categorical column. If a category is less frequent than rare_to_value * len(X), it is replaced with the string in rare_value. Use this parameter to group rare categories before encoding the column. If None, ignores this step.

rare_value: str, default=”rare”

Value with which to replace rare categories. Ignored when rare_to_value is None.

polynomial_features: bool, default = False

When set to True, new features are derived using existing numeric features.

polynomial_degree: int, default = 2

Degree of polynomial features. For example, if an input sample is two dimensional and of the form [a, b], the polynomial features with degree = 2 are: [1, a, b, a^2, ab, b^2]. Ignored when polynomial_features is not True.

low_variance_threshold: float or None, default = None

Remove features with a training-set variance lower than the provided threshold. If 0, keep all features with non-zero variance, i.e. remove the features that have the same value in all samples. If None, skip this transformation step.

group_features: list, list of lists or None, default = None

When the dataset contains features with related characteristics, add new fetaures with the following statistical properties of that group: min, max, mean, std, median and mode. The parameter takes a list of feature names or a list of lists of feature names to specify multiple groups.

group_names: str, list, or None, default = None

Group names to be used when naming the new features. The length should match with the number of groups specified in group_features. If None, new features are named using the default form, e.g. group_1, group_2, etc… Ignored when group_features is None.

drop_groups: bool, default=False

Whether to drop the original features in the group. Ignored when group_features is None.

remove_multicollinearity: bool, default = False

When set to True, features with the inter-correlations higher than the defined threshold are removed. For each group, it removes all except the feature with the highest correlation to y.

multicollinearity_threshold: float, default = 0.9

Minimum absolute Pearson correlation to identify correlated features. The default value removes equal columns. Ignored when remove_multicollinearity is not True.

bin_numeric_features: list of str, default = None

To convert numeric features into categorical, bin_numeric_features parameter can be used. It takes a list of strings with column names to be discretized. It does so by using ‘sturges’ rule to determine the number of clusters and then apply KMeans algorithm. Original values of the feature are then replaced by the cluster label.

remove_outliers: bool, default = False

When set to True, outliers from the training data are removed using an Isolation Forest.

outliers_method: str, default = “iforest”

Method with which to remove outliers. Ignored when remove_outliers=False. Possible values are:

‘iforest’: Uses sklearn’s IsolationForest.

‘ee’: Uses sklearn’s EllipticEnvelope.

‘lof’: Uses sklearn’s LocalOutlierFactor.

outliers_threshold: float, default = 0.05

The percentage of outliers to be removed from the dataset. Ignored when remove_outliers=False.

transformation: bool, default = False

When set to True, it applies the power transform to make data more Gaussian-like. Type of transformation is defined by the transformation_method parameter.

transformation_method: str, default = ‘yeo-johnson’

Defines the method for transformation. By default, the transformation method is set to ‘yeo-johnson’. The other available option for transformation is ‘quantile’. Ignored when transformation is not True.

normalize: bool, default = False

When set to True, it transforms the features by scaling them to a given range. Type of scaling is defined by the normalize_method parameter.

normalize_method: str, default = ‘zscore’

Defines the method for scaling. By default, normalize method is set to ‘zscore’ The standard zscore is calculated as z = (x - u) / s. Ignored when normalize is not True. The other options are:

minmax: scales and translates each feature individually such that it is in

the range of 0 - 1. - maxabs: scales and translates each feature individually such that the maximal absolute value of each feature will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity. - robust: scales and translates each feature according to the Interquartile range. When the dataset contains outliers, robust scaler often gives better results.

pca: bool, default = False

When set to True, dimensionality reduction is applied to project the data into a lower dimensional space using the method defined in pca_method parameter.

pca_method: str, default = ‘linear’

Method with which to apply PCA. Possible values are:

‘linear’: Uses Singular Value Decomposition.
‘kernel’: Dimensionality reduction through the use of RBF kernel.
‘incremental’: Similar to ‘linear’, but more efficient for large datasets.

pca_components: int, float, str or None, default = None

Number of components to keep. This parameter is ignored when pca=False.

If None: All components are kept.
If int: Absolute number of components.
If float: Such an amount that the variance that needs to be explained
is greater than the percentage specified by n_components. Value should lie between 0 and 1 (ony for pca_method=’linear’).
If “mle”: Minka’s MLE is used to guess the dimension (ony for pca_method=’linear’).

feature_selection: bool, default = False

When set to True, a subset of features is selected based on a feature importance score determined by feature_selection_estimator.

feature_selection_method: str, default = ‘classic’

Algorithm for feature selection. Choose from:

‘univariate’: Uses sklearn’s SelectKBest.
‘classic’: Uses sklearn’s SelectFromModel.
‘sequential’: Uses sklearn’s SequentialFeatureSelector.

feature_selection_estimator: str or sklearn estimator, default = ‘lightgbm’

Classifier used to determine the feature importances. The estimator should have a feature_importances_ or coef_ attribute after fitting. If None, it uses LGBRegressor. This parameter is ignored when feature_selection_method=univariate.

n_features_to_select: int or float, default = 0.2

The maximum number of features to select with feature_selection. If <1, it’s the fraction of starting features. Note that this parameter doesn’t take features in ignore_features or keep_features into account when counting.

transform_target: bool, default = False

When set to True, target variable is transformed using the method defined in transform_target_method param. Target transformation is applied separately from feature transformations.

transform_target_method: str, default = ‘yeo-johnson’

Defines the method for transformation. By default, the transformation method is set to ‘yeo-johnson’. The other available option for transformation is ‘quantile’. Ignored when transform_target is not True.

custom_pipeline: list of (str, transformer), dict or Pipeline, default = None

Addidiotnal custom transformers. If passed, they are applied to the pipeline last, after all the build-in transformers.

custom_pipeline_position: int, default = -1

Position of the custom pipeline in the overal preprocessing pipeline. The default value adds the custom pipeline last.

data_split_shuffle: bool, default = True

When set to False, prevents shuffling of rows during ‘train_test_split’.

data_split_stratify: bool or list, default = False

Controls stratification during ‘train_test_split’. When set to True, will stratify by target column. To stratify on any other columns, pass a list of column names. Ignored when data_split_shuffle is False.

fold_strategy: str or sklearn CV generator object, default = ‘kfold’

Choice of cross validation strategy. Possible values are:

‘kfold’
‘groupkfold’
‘timeseries’
a custom CV generator object compatible with scikit-learn.

For groupkfold, column name must be passed in fold_groups parameter. Example: setup(fold_strategy="groupkfold", fold_groups="COLUMN_NAME")

fold: int, default = 10

Number of folds to be used in cross validation. Must be at least 2. This is a global setting that can be over-written at function level by using fold parameter. Ignored when fold_strategy is a custom object.

fold_shuffle: bool, default = False

Controls the shuffle parameter of CV. Only applicable when fold_strategy is ‘kfold’ or ‘stratifiedkfold’. Ignored when fold_strategy is a custom object.

fold_groups: str or array-like, with shape (n_samples,), default = None

Optional group labels when ‘GroupKFold’ is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.

n_jobs: int, default = -1

The number of jobs to run in parallel (for functions that supports parallel processing) -1 means using all processors. To run all functions on single processor set n_jobs to None.

use_gpu: bool or str, default = False

When set to True, it will use GPU for training with algorithms that support it, and fall back to CPU if they are unavailable. When set to ‘force’, it will only use GPU-enabled algorithms and raise exceptions when they are unavailable. When False, all algorithms are trained using CPU only.

GPU enabled algorithms:

Extreme Gradient Boosting, requires no further installation
CatBoost Classifier, requires no further installation

(GPU is only enabled when data > 50,000 rows)

Light Gradient Boosting Machine, requires GPU installation

https://lightgbm.readthedocs.io/en/latest/GPU-Tutorial.html

Linear Regression, Lasso Regression, Ridge Regression, K Neighbors Regressor,

Random Forest, Support Vector Regression, Elastic Net requires cuML >= 0.15 https://github.com/rapidsai/cuml

html: bool, default = True

When set to False, prevents runtime display of monitor. This must be set to False when the environment does not support IPython. For example, command line terminal, Databricks Notebook, Spyder and other similar IDEs.

session_id: int, default = None

Controls the randomness of experiment. It is equivalent to ‘random_state’ in scikit-learn. When None, a pseudo random number is generated. This can be used for later reproducibility of the entire experiment.

log_experiment: bool, default = False

A (list of) PyCaret BaseLogger or str (one of ‘mlflow’, ‘wandb’, ‘comet_ml’) corresponding to a logger to determine which experiment loggers to use. Setting to True will use just MLFlow. If wandb (Weights & Biases) or comet_ml is installed, will also log there.

system_log: bool or str or logging.Logger, default = True

Whether to save the system logging file (as logs.log). If the input is a string, use that as the path to the logging file. If the input already is a logger object, use that one instead.

experiment_name: str, default = None

Name of the experiment for logging. Ignored when log_experiment is False.

experiment_custom_tags: dict, default = None

Dictionary of tag_name: String -> value: (String, but will be string-ified if not) passed to the mlflow.set_tags to add new custom tags for the experiment.

log_plots: bool or list, default = False

When set to True, certain plots are logged automatically in the MLFlow server. To change the type of plots to be logged, pass a list containing plot IDs. Refer to documentation of plot_model. Ignored when log_experiment is False.

log_profile: bool, default = False

When set to True, data profile is logged on the MLflow server as a html file. Ignored when log_experiment is False.

log_data: bool, default = False

When set to True, dataset is logged on the MLflow server as a csv file. Ignored when log_experiment is False.

engine: Optional[Dict[str, str]] = None

The execution engines to use for the models in the form of a dict of model_id: engine - e.g. for Linear Regression (“lr”, users can switch between “sklearn” and “sklearnex” by specifying engine={“lr”: “sklearnex”}

verbose: bool, default = True

When set to False, Information grid is not printed.

memory: str, bool or Memory, default=True

Used to cache the fitted transformers of the pipeline.: If False: No caching is performed. If True: A default temp directory is used. If str: Path to the caching directory.

profile: bool, default = False

When set to True, an interactive EDA report is displayed.

profile_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the ProfileReport method used to create the EDA report. Ignored if profile is False.

Returns: Global variables that can be changed using the set_config function.

compare_models(include: Optional[List[Union[str, Any]]] = None, exclude: Optional[List[str]] = None, fold: Optional[Union[int, Any]] = None, round: int = 4, cross_validation: bool = True, sort: str = 'R2', n_select: int = 1, budget_time: Optional[float] = None, turbo: bool = True, errors: str = 'ignore', fit_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, experiment_custom_tags: Optional[Dict[str, Any]] = None, engine: Optional[Dict[str, str]] = None, verbose: bool = True, parallel: Optional[ParallelBackend] = None)

This function trains and evaluates performance of all estimators available in the model library using cross validation. The output of this function is a score grid with average cross validated scores. Metrics evaluated during CV can be accessed using the get_metrics function. Custom metrics can be added or removed using add_metric and remove_metric function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> best_model = compare_models()

include: list of str or scikit-learn compatible object, default = None: To train and evaluate select models, list containing model ID or scikit-learn compatible object can be passed in include param. To see a list of all models available in the model library use the models function.
exclude: list of str, default = None: To omit certain models from training and evaluation, pass a list containing model id in the exclude parameter. To see a list of all models available in the model library use the models function.
fold: int or scikit-learn compatible CV generator, default = None: Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.
round: int, default = 4: Number of decimal places the metrics in the score grid will be rounded to.
cross_validation: bool, default = True: When set to False, metrics are evaluated on holdout set. fold param is ignored when cross_validation is set to False.
sort: str, default = ‘R2’: The sort order of the score grid. It also accepts custom metrics that are added through the add_metric function.
n_select: int, default = 1: Number of top_n models to return. For example, to select top 3 models use n_select = 3.
budget_time: int or float, default = None: If not None, will terminate execution of the function after budget_time minutes have passed and return results up to that point.
turbo: bool, default = True: When set to True, it excludes estimators with longer training times. To see which algorithms are excluded use the models function.
errors: str, default = ‘ignore’: When set to ‘ignore’, will skip the model with exceptions and continue. If ‘raise’, will break the function when exceptions are raised.
fit_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the fit method of the model.
groups: str or array-like, with shape (n_samples,), default = None: Optional group labels when ‘GroupKFold’ is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.
engine: Optional[Dict[str, str]] = None: The execution engines to use for the models in the form of a dict of model_id: engine - e.g. for Linear Regression (“lr”, users can switch between “sklearn” and “sklearnex” by specifying engine={“lr”: “sklearnex”}
verbose: bool, default = True: Score grid is not printed when verbose is set to False.
parallel: pycaret.internal.parallel.parallel_backend.ParallelBackend, default = None: A ParallelBackend instance. For example if you have a SparkSession session, you can use FugueBackend(session) to make this function running using Spark. For more details, see FugueBackend

Returns: Trained model or list of trained models, depending on the n_select param.

Warning

Changing turbo parameter to False may result in very high training times with

datasets exceeding 10,000 rows.

No models are logged in MLFlow when cross_validation parameter is False.

create_model(estimator: Union[str, Any], fold: Optional[Union[int, Any]] = None, round: int = 4, cross_validation: bool = True, fit_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, experiment_custom_tags: Optional[Dict[str, Any]] = None, engine: Optional[str] = None, verbose: bool = True, return_train_score: bool = False, **kwargs)

This function trains and evaluates the performance of a given estimator using cross validation. The output of this function is a score grid with CV scores by fold. Metrics evaluated during CV can be accessed using the get_metrics function. Custom metrics can be added or removed using add_metric and remove_metric function. All the available models can be accessed using the models function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')

estimator: str or scikit-learn compatible object

ID of an estimator available in model library or pass an untrained model object consistent with scikit-learn API. Estimators available in the model library (ID - Name):

‘lr’ - Linear Regression
‘lasso’ - Lasso Regression
‘ridge’ - Ridge Regression
‘en’ - Elastic Net
‘lar’ - Least Angle Regression
‘llar’ - Lasso Least Angle Regression
‘omp’ - Orthogonal Matching Pursuit
‘br’ - Bayesian Ridge
‘ard’ - Automatic Relevance Determination
‘par’ - Passive Aggressive Regressor
‘ransac’ - Random Sample Consensus
‘tr’ - TheilSen Regressor
‘huber’ - Huber Regressor
‘kr’ - Kernel Ridge
‘svm’ - Support Vector Regression
‘knn’ - K Neighbors Regressor
‘dt’ - Decision Tree Regressor
‘rf’ - Random Forest Regressor
‘et’ - Extra Trees Regressor
‘ada’ - AdaBoost Regressor
‘gbr’ - Gradient Boosting Regressor
‘mlp’ - MLP Regressor
‘xgboost’ - Extreme Gradient Boosting
‘lightgbm’ - Light Gradient Boosting Machine
‘catboost’ - CatBoost Regressor

fold: int or scikit-learn compatible CV generator, default = None

Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.

round: int, default = 4

Number of decimal places the metrics in the score grid will be rounded to.

cross_validation: bool, default = True

When set to False, metrics are evaluated on holdout set. fold param is ignored when cross_validation is set to False.

fit_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the fit method of the model.

groups: str or array-like, with shape (n_samples,), default = None

Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.

experiment_custom_tags: dict, default = None

Dictionary of tag_name: String -> value: (String, but will be string-ified if not) passed to the mlflow.set_tags to add new custom tags for the experiment.

engine: Optional[str] = None

The execution engine to use for the model, e.g. for Linear Regression (“lr”), users can switch between “sklearn” and “sklearnex” by specifying engine=”sklearnex”.

verbose: bool, default = True

Score grid is not printed when verbose is set to False.

return_train_score: bool, default = False

If False, returns the CV Validation scores only. If True, returns the CV training scores along with the CV validation scores. This is useful when the user wants to do bias-variance tradeoff. A high CV training score with a low corresponding CV validation score indicates overfitting.

**kwargs:

Additional keyword arguments to pass to the estimator.

Returns: Trained Model

Warning

Models are not logged on the MLFlow server when cross_validation param

is set to False.

tune_model(estimator, fold: Optional[Union[int, Any]] = None, round: int = 4, n_iter: int = 10, custom_grid: Optional[Union[Dict[str, list], Any]] = None, optimize: str = 'R2', custom_scorer=None, search_library: str = 'scikit-learn', search_algorithm: Optional[str] = None, early_stopping: Any = False, early_stopping_max_iters: int = 10, choose_better: bool = True, fit_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, return_tuner: bool = False, verbose: bool = True, tuner_verbose: Union[int, bool] = True, return_train_score: bool = False, **kwargs)

This function tunes the hyperparameters of a given estimator. The output of this function is a score grid with CV scores by fold of the best selected model based on optimize parameter. Metrics evaluated during CV can be accessed using the get_metrics function. Custom metrics can be added or removed using add_metric and remove_metric function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> tuned_lr = tune_model(lr)

estimator: scikit-learn compatible object

Trained model object

fold: int or scikit-learn compatible CV generator, default = None

Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.

round: int, default = 4

Number of decimal places the metrics in the score grid will be rounded to.

n_iter: int, default = 10

Number of iterations in the grid search. Increasing ‘n_iter’ may improve model performance but also increases the training time.

custom_grid: dictionary, default = None

To define custom search space for hyperparameters, pass a dictionary with parameter name and values to be iterated. Custom grids must be in a format supported by the defined search_library.

optimize: str, default = ‘R2’

Metric name to be evaluated for hyperparameter tuning. It also accepts custom metrics that are added through the add_metric function.

custom_scorer: object, default = None

custom scoring strategy can be passed to tune hyperparameters of the model. It must be created using sklearn.make_scorer. It is equivalent of adding custom metric using the add_metric function and passing the name of the custom metric in the optimize parameter. Will be deprecated in future.

search_library: str, default = ‘scikit-learn’

The search library used for tuning hyperparameters. Possible values:

‘scikit-learn’ - default, requires no further installation
https://github.com/scikit-learn/scikit-learn
‘scikit-optimize’ - pip install scikit-optimize
https://scikit-optimize.github.io/stable/
‘tune-sklearn’ - pip install tune-sklearn ray[tune]
https://github.com/ray-project/tune-sklearn
‘optuna’ - pip install optuna
https://optuna.org/

search_algorithm: str, default = None

The search algorithm depends on the search_library parameter. Some search algorithms require additional libraries to be installed. If None, will use search library-specific default algorithm.

‘scikit-learn’ possible values:
- ‘random’ : random grid search (default)
- ‘grid’ : grid search
‘scikit-optimize’ possible values:
- ‘bayesian’ : Bayesian search (default)
‘tune-sklearn’ possible values:
- ‘random’ : random grid search (default)
- ‘grid’ : grid search
- ‘bayesian’ : pip install scikit-optimize
- ‘hyperopt’ : pip install hyperopt
- ‘optuna’ : pip install optuna
- ‘bohb’ : pip install hpbandster ConfigSpace
‘optuna’ possible values:
- ‘random’ : randomized search
- ‘tpe’ : Tree-structured Parzen Estimator search (default)

early_stopping: bool or str or object, default = False

Use early stopping to stop fitting to a hyperparameter configuration if it performs poorly. Ignored when search_library is scikit-learn, or if the estimator does not have ‘partial_fit’ attribute. If False or None, early stopping will not be used. Can be either an object accepted by the search library or one of the following:

‘asha’ for Asynchronous Successive Halving Algorithm
‘hyperband’ for Hyperband
‘median’ for Median Stopping Rule
If False or None, early stopping will not be used.

early_stopping_max_iters: int, default = 10

Maximum number of epochs to run for each sampled configuration. Ignored if early_stopping is False or None.

choose_better: bool, default = True

When set to True, the returned object is always better performing. The metric used for comparison is defined by the optimize parameter.

fit_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the fit method of the tuner.

groups: str or array-like, with shape (n_samples,), default = None

Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.

return_tuner: bool, default = False

When set to True, will return a tuple of (model, tuner_object).

verbose: bool, default = True

Score grid is not printed when verbose is set to False.

tuner_verbose: bool or in, default = True

If True or above 0, will print messages from the tuner. Higher values print more messages. Ignored when verbose param is False.

return_train_score: bool, default = False

If False, returns the CV Validation scores only. If True, returns the CV training scores along with the CV validation scores. This is useful when the user wants to do bias-variance tradeoff. A high CV training score with a low corresponding CV validation score indicates overfitting.

**kwargs:

Additional keyword arguments to pass to the optimizer.

Returns: Trained Model and Optional Tuner Object when return_tuner is True.

Warning

Using ‘grid’ as search_algorithm may result in very long computation.

Only recommended with smaller search spaces that can be defined in the custom_grid parameter.

search_library ‘tune-sklearn’ does not support GPU models.

ensemble_model(estimator, method: str = 'Bagging', fold: Optional[Union[int, Any]] = None, n_estimators: int = 10, round: int = 4, choose_better: bool = False, optimize: str = 'R2', fit_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, verbose: bool = True, return_train_score: bool = False) → Any

This function ensembles a given estimator. The output of this function is a score grid with CV scores by fold. Metrics evaluated during CV can be accessed using the get_metrics function. Custom metrics can be added or removed using add_metric and remove_metric function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> dt = create_model('dt')
>>> bagged_dt = ensemble_model(dt, method = 'Bagging')

estimator: scikit-learn compatible object: Trained model object
method: str, default = ‘Bagging’: Method for ensembling base estimator. It can be ‘Bagging’ or ‘Boosting’.
fold: int or scikit-learn compatible CV generator, default = None: Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.
n_estimators: int, default = 10: The number of base estimators in the ensemble. In case of perfect fit, the learning procedure is stopped early.
round: int, default = 4: Number of decimal places the metrics in the score grid will be rounded to.
choose_better: bool, default = False: When set to True, the returned object is always better performing. The metric used for comparison is defined by the optimize parameter.
optimize: str, default = ‘R2’: Metric to compare for model selection when choose_better is True.
fit_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the fit method of the model.
groups: str or array-like, with shape (n_samples,), default = None: Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.

return_train_score: bool, default = False If False, returns the CV Validation scores only. If True, returns the CV training scores along with the CV validation scores. This is useful when the user wants to do bias-variance tradeoff. A high CV training score with a low corresponding CV validation score indicates overfitting.

verbose: bool, default = True: Score grid is not printed when verbose is set to False.

Returns: Trained Model

blend_models(estimator_list: list, fold: Optional[Union[int, Any]] = None, round: int = 4, choose_better: bool = False, optimize: str = 'R2', weights: Optional[List[float]] = None, fit_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, verbose: bool = True, return_train_score: bool = False)

This function trains a Voting Regressor for select models passed in the estimator_list param. The output of this function is a score grid with CV scores by fold. Metrics evaluated during CV can be accessed using the get_metrics function. Custom metrics can be added or removed using add_metric and remove_metric function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> top3 = compare_models(n_select = 3)
>>> blender = blend_models(top3)

estimator_list: list of scikit-learn compatible objects: List of trained model objects
fold: int or scikit-learn compatible CV generator, default = None: Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.
round: int, default = 4: Number of decimal places the metrics in the score grid will be rounded to.
choose_better: bool, default = False: When set to True, the returned object is always better performing. The metric used for comparison is defined by the optimize parameter.
optimize: str, default = ‘R2’: Metric to compare for model selection when choose_better is True.
weights: list, default = None: Sequence of weights (float or int) to weight the occurrences of predicted class labels (hard voting) or class probabilities before averaging (soft voting). Uses uniform weights when None.
fit_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the fit method of the model.
groups: str or array-like, with shape (n_samples,), default = None: Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.
verbose: bool, default = True: Score grid is not printed when verbose is set to False.
return_train_score: bool, default = False: If False, returns the CV Validation scores only. If True, returns the CV training scores along with the CV validation scores. This is useful when the user wants to do bias-variance tradeoff. A high CV training score with a low corresponding CV validation score indicates overfitting.

Returns: Trained Model

stack_models(estimator_list: list, meta_model=None, meta_model_fold: Optional[Union[int, Any]] = 5, fold: Optional[Union[int, Any]] = None, round: int = 4, restack: bool = False, choose_better: bool = False, optimize: str = 'R2', fit_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, verbose: bool = True, return_train_score: bool = False)

This function trains a meta model over select estimators passed in the estimator_list parameter. The output of this function is a score grid with CV scores by fold. Metrics evaluated during CV can be accessed using the get_metrics function. Custom metrics can be added or removed using add_metric and remove_metric function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> top3 = compare_models(n_select = 3)
>>> stacker = stack_models(top3)

estimator_list: list of scikit-learn compatible objects: List of trained model objects
meta_model: scikit-learn compatible object, default = None: When None, Linear Regression is trained as a meta model.
meta_model_fold: integer or scikit-learn compatible CV generator, default = 5: Controls internal cross-validation. Can be an integer or a scikit-learn CV generator. If set to an integer, will use (Stratifed)KFold CV with that many folds. See scikit-learn documentation on Stacking for more details.
fold: int or scikit-learn compatible CV generator, default = None: Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.
round: int, default = 4: Number of decimal places the metrics in the score grid will be rounded to.
restack: bool, default = False: When set to False, only the predictions of estimators will be used as training data for the meta_model.
choose_better: bool, default = False: When set to True, the returned object is always better performing. The metric used for comparison is defined by the optimize parameter.
optimize: str, default = ‘R2’: Metric to compare for model selection when choose_better is True.
fit_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the fit method of the model.
groups: str or array-like, with shape (n_samples,), default = None: Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.
verbose: bool, default = True: Score grid is not printed when verbose is set to False.
return_train_score: bool, default = False: If False, returns the CV Validation scores only. If True, returns the CV training scores along with the CV validation scores. This is useful when the user wants to do bias-variance tradeoff. A high CV training score with a low corresponding CV validation score indicates overfitting.

Returns: Trained Model

plot_model(estimator, plot: str = 'residuals', scale: float = 1, save: bool = False, fold: Optional[Union[int, Any]] = None, fit_kwargs: Optional[dict] = None, plot_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, use_train_data: bool = False, verbose: bool = True, display_format: Optional[str] = None) → Optional[str]

This function analyzes the performance of a trained model on holdout set. It may require re-training the model in certain cases.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> plot_model(lr, plot = 'residual')

estimator: scikit-learn compatible object

Trained model object

plot: str, default = ‘residual’

List of available plots (ID - Name):

‘pipeline’ - Schematic drawing of the preprocessing pipeline
‘residuals_interactive’ - Interactive Residual plots
‘residuals’ - Residuals Plot
‘error’ - Prediction Error Plot
‘cooks’ - Cooks Distance Plot
‘rfe’ - Recursive Feat. Selection
‘learning’ - Learning Curve
‘vc’ - Validation Curve
‘manifold’ - Manifold Learning
‘feature’ - Feature Importance
‘feature_all’ - Feature Importance (All)
‘parameter’ - Model Hyperparameter
‘tree’ - Decision Tree

scale: float, default = 1

The resolution scale of the figure.

save: bool, default = False

When set to True, plot is saved in the current working directory.

fold: int or scikit-learn compatible CV generator, default = None

Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.

fit_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the fit method of the model.

plot_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the visualizer class.

pipeline: fontsize -> int

plot_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the visualizer class.

pipeline: fontsize -> int

groups: str or array-like, with shape (n_samples,), default = None

Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.

use_train_data: bool, default = False

When set to true, train data will be used for plots, instead of test data.

verbose: bool, default = True

When set to False, progress bar is not displayed.

display_format: str, default = None

To display plots in Streamlit (https://www.streamlit.io/), set this to ‘streamlit’. Currently, not all plots are supported.

Returns: Path to saved file, if any.

evaluate_model(estimator, fold: Optional[Union[int, Any]] = None, fit_kwargs: Optional[dict] = None, plot_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, use_train_data: bool = False)

This function displays a user interface for analyzing performance of a trained model. It calls the plot_model function internally.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> evaluate_model(lr)

estimator: scikit-learn compatible object: Trained model object
fold: int or scikit-learn compatible CV generator, default = None: Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.
fit_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the fit method of the model.
plot_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the visualizer class.
groups: str or array-like, with shape (n_samples,), default = None: Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.
use_train_data: bool, default = False: When set to true, train data will be used for plots, instead of test data.

Returns: None

Warning

This function only works in IPython enabled Notebook.

interpret_model(estimator, plot: str = 'summary', feature: Optional[str] = None, observation: Optional[int] = None, use_train_data: bool = False, X_new_sample: Optional[DataFrame] = None, y_new_sample: Optional[DataFrame] = None, save: Union[str, bool] = False, **kwargs)

This function takes a trained model object and returns an interpretation plot based on the test / hold-out set.

This function is implemented based on the SHAP (SHapley Additive exPlanations), which is a unified approach to explain the output of any machine learning model. SHAP connects game theory with local explanations.

For more information: https://shap.readthedocs.io/en/latest/

For more information on Partial Dependence Plot: https://github.com/SauceCat/PDPbox

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp = setup(data = boston,  target = 'medv')
>>> xgboost = create_model('xgboost')
>>> interpret_model(xgboost)

estimator: scikit-learn compatible object

Trained model object

plotstr, default = ‘summary’

Abbreviation of type of plot. The current list of plots supported are (Plot - Name):

‘summary’ - Summary Plot using SHAP
‘correlation’ - Dependence Plot using SHAP
‘reason’ - Force Plot using SHAP
‘pdp’ - Partial Dependence Plot
‘msa’ - Morris Sensitivity Analysis
‘pfi’ - Permutation Feature Importance

feature: str, default = None

This parameter is only needed when plot = ‘correlation’ or ‘pdp’. By default feature is set to None which means the first column of the dataset will be used as a variable. A feature parameter must be passed to change this.

observation: integer, default = None

This parameter only comes into effect when plot is set to ‘reason’. If no observation number is provided, it will return an analysis of all observations with the option to select the feature on x and y axes through drop down interactivity. For analysis at the sample level, an observation parameter must be passed with the index value of the observation in test / hold-out set.

use_train_data: bool, default = False

When set to true, train data will be used for plots, instead of test data.

X_new_sample: pd.DataFrame, default = None

Row from an out-of-sample dataframe (neither train nor test data) to be plotted. The sample must have the same columns as the raw input train data, and it is transformed by the preprocessing pipeline automatically before plotting.

y_new_sample: pd.DataFrame, default = None

Row from an out-of-sample dataframe (neither train nor test data) to be plotted. The sample must have the same columns as the raw input label data, and it is transformed by the preprocessing pipeline automatically before plotting.

save: string or bool, default = False

When set to True, Plot is saved as a ‘png’ file in current working directory. When a path destination is given, Plot is saved as a ‘png’ file the given path to the directory of choice.

**kwargs:

Additional keyword arguments to pass to the plot.

Returns: None

predict_model(estimator, data: Optional[DataFrame] = None, round: int = 4, verbose: bool = True) → DataFrame

This function predicts Label using a trained model. When data is None, it predicts label on the holdout set.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> pred_holdout = predict_model(lr)
>>> pred_unseen = predict_model(lr, data = unseen_dataframe)

estimator: scikit-learn compatible object: Trained model object
datapandas.DataFrame: Shape (n_samples, n_features). All features used during training must be available in the unseen dataset.
round: int, default = 4: Number of decimal places to round predictions to.
verbose: bool, default = True: When set to False, holdout score grid is not printed.

Returns: pandas.DataFrame

Warning

The behavior of the predict_model is changed in version 2.1 without backward

compatibility. As such, the pipelines trained using the version (<= 2.0), may not work for inference with version >= 2.1. You can either retrain your models with a newer version or downgrade the version for inference.

finalize_model(estimator, fit_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, model_only: bool = False, experiment_custom_tags: Optional[Dict[str, Any]] = None) → Any

This function trains a given estimator on the entire dataset including the holdout set.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> final_lr = finalize_model(lr)

estimator: scikit-learn compatible object: Trained model object
fit_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the fit method of the model.
groups: str or array-like, with shape (n_samples,), default = None: Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.
model_onlybool, default = False: Whether to return the complete fitted pipeline or only the fitted model.
experiment_custom_tags: dict, default = None: Dictionary of tag_name: String -> value: (String, but will be string-ified if not) passed to the mlflow.set_tags to add new custom tags for the experiment.

Returns: Trained pipeline or model object fitted on complete dataset.

deploy_model(model, model_name: str, authentication: dict, platform: str = 'aws')

This function deploys the transformation pipeline and trained model on cloud.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> # sets appropriate credentials for the platform as environment variables
>>> import os
>>> os.environ["AWS_ACCESS_KEY_ID"] = str("foo")
>>> os.environ["AWS_SECRET_ACCESS_KEY"] = str("bar")
>>> deploy_model(model = lr, model_name = 'lr-for-deployment', platform = 'aws', authentication = {'bucket' : 'S3-bucket-name'})

Amazon Web Service (AWS) users:

To deploy a model on AWS S3 (‘aws’), the credentials have to be passed. The easiest way is to use environment variables in your local environment. Following information from the IAM portal of amazon console account are required:

AWS Access Key ID
AWS Secret Key Access

More info: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#environment-variables

Google Cloud Platform (GCP) users:

To deploy a model on Google Cloud Platform (‘gcp’), project must be created using command line or GCP console. Once project is created, you must create a service account and download the service account key as a JSON file to set environment variables in your local environment.

More info: https://cloud.google.com/docs/authentication/production

Microsoft Azure (Azure) users:

To deploy a model on Microsoft Azure (‘azure’), environment variables for connection string must be set in your local environment. Go to settings of storage account on Azure portal to access the connection string required.

AZURE_STORAGE_CONNECTION_STRING (required as environment variable)

More info: https://docs.microsoft.com/en-us/azure/storage/blobs/storage-quickstart-blobs-python?toc=%2Fpython%2Fazure%2FTOC.json

model: scikit-learn compatible object

Trained model object

model_name: str

Name of model.

authentication: dict

Dictionary of applicable authentication tokens.

When platform = ‘aws’: {‘bucket’ : ‘S3-bucket-name’, ‘path’: (optional) folder name under the bucket}

When platform = ‘gcp’: {‘project’: ‘gcp-project-name’, ‘bucket’ : ‘gcp-bucket-name’}

When platform = ‘azure’: {‘container’: ‘azure-container-name’}

platform: str, default = ‘aws’

Name of the platform. Currently supported platforms: ‘aws’, ‘gcp’ and ‘azure’.

Returns: None

save_model(model, model_name: str, model_only: bool = False, verbose: bool = True, **kwargs)

This function saves the transformation pipeline and trained model object into the current working directory as a pickle file for later use.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> save_model(lr, 'saved_lr_model')

model: scikit-learn compatible object: Trained model object
model_name: str: Name of the model.
model_only: bool, default = False: When set to True, only trained model object is saved instead of the entire pipeline.
**kwargs:: Additional keyword arguments to pass to joblib.dump().
verbose: bool, default = True: Success message is not printed when verbose is set to False.

Returns: Tuple of the model object and the filename.

load_model(model_name: str, platform: Optional[str] = None, authentication: Optional[Dict[str, str]] = None, verbose: bool = True)

This function loads a previously saved pipeline.

Example

>>> from pycaret.regression import load_model
>>> saved_lr = load_model('saved_lr_model')

model_name: str

Name of the model.

platform: str, default = None

Name of the cloud platform. Currently supported platforms: ‘aws’, ‘gcp’ and ‘azure’.

authentication: dict, default = None

dictionary of applicable authentication tokens.

when platform = ‘aws’: {‘bucket’ : ‘Name of Bucket on S3’, ‘path’: (optional) folder name under the bucket}

when platform = ‘gcp’: {‘project’: ‘gcp-project-name’, ‘bucket’ : ‘gcp-bucket-name’}

when platform = ‘azure’: {‘container’: ‘azure-container-name’}

verbose: bool, default = True

Success message is not printed when verbose is set to False.

Returns: Trained Model

automl(optimize: str = 'Accuracy', use_holdout: bool = False, turbo: bool = True, return_train_score: bool = False) → Any

This function returns the best model out of all trained models in current session based on the optimize parameter. Metrics evaluated can be accessed using the get_metrics function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> top3 = compare_models(n_select = 3)
>>> tuned_top3 = [tune_model(i) for i in top3]
>>> blender = blend_models(tuned_top3)
>>> stacker = stack_models(tuned_top3)
>>> best_mae_model = automl(optimize = 'MAE')

optimize: str, default = ‘R2’: Metric to use for model selection. It also accepts custom metrics added using the add_metric function.
use_holdout: bool, default = False: When set to True, metrics are evaluated on holdout set instead of CV.
turbo: bool, default = True: When set to True and use_holdout is False, only models created with default fold parameter will be considered. If set to False, models created with a non-default fold parameter will be scored again using default fold settings, so that they can be compared.
return_train_score: bool, default = False: If False, returns the CV Validation scores only. If True, returns the CV training scores along with the CV validation scores. This is useful when the user wants to do bias-variance tradeoff. A high CV training score with a low corresponding CV validation score indicates overfitting.

Returns: Trained Model

models(type: Optional[str] = None, internal: bool = False, raise_errors: bool = True) → DataFrame

Returns table of models available in the model library.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> all_models = models()

type: str, default = None

linear : filters and only return linear models
tree : filters and only return tree based models
ensemble : filters and only return ensemble models

internal: bool, default = False

When True, will return extra columns and rows used internally.

raise_errors: bool, default = True

When False, will suppress all exceptions, ignoring models that couldn’t be created.

Returns: pandas.DataFrame

get_metrics(reset: bool = False, include_custom: bool = True, raise_errors: bool = True) → DataFrame

Returns table of available metrics used in the experiment.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> all_metrics = get_metrics()

reset: bool, default = False: When True, will reset all changes made using the add_metric and remove_metric function.
include_custom: bool, default = True: Whether to include user added (custom) metrics or not.
raise_errors: bool, default = True: If False, will suppress all exceptions, ignoring models that couldn’t be created.

Returns: pandas.DataFrame

add_metric(id: str, name: str, score_func: type, greater_is_better: bool = True, **kwargs) → Series

Adds a custom metric to be used in the experiment.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> from sklearn.metrics import explained_variance_score
>>> add_metric('evs', 'EVS', explained_variance_score)

id: str: Unique id for the metric.
name: str: Display name of the metric.
score_func: type: Score function (or loss function) with signature score_func(y, y_pred, **kwargs).
greater_is_better: bool, default = True: Whether score_func is higher the better or not.
**kwargs:: Arguments to be passed to score function.

Returns: pandas.Series

remove_metric(name_or_id: str)

Removes a metric from experiment.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'mredv')
>>> remove_metric('MAPE')

name_or_id: str: Display name or ID of the metric.

Returns: None

get_logs(experiment_name: Optional[str] = None, save: bool = False) → DataFrame

Returns a table of experiment logs. Only works when log_experiment is True when initializing the setup function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv', log_experiment = True)
>>> best = compare_models()
>>> exp_logs = get_logs()

experiment_name: str, default = None: When None current active run is used.
save: bool, default = False: When set to True, csv file is saved in current working directory.

Returns: pandas.DataFrame

dashboard(estimator, display_format: str = 'dash', dashboard_kwargs: Optional[Dict[str, Any]] = None, run_kwargs: Optional[Dict[str, Any]] = None, **kwargs)

This function generates the interactive dashboard for a trained model. The dashboard is implemented using ExplainerDashboard (explainerdashboard.readthedocs.io)

Example

>>> from pycaret.datasets import get_data
>>> juice = get_data('juice')
>>> from pycaret.classification import *
>>> exp_name = setup(data = juice,  target = 'Purchase')
>>> lr = create_model('lr')
>>> dashboard(lr)

estimator: scikit-learn compatible object

Trained model object

display_format: str, default = ‘dash’

Render mode for the dashboard. The default is set to dash which will render a dashboard in browser. There are four possible options:

‘dash’ - displays the dashboard in browser
‘inline’ - displays the dashboard in the jupyter notebook cell.
‘jupyterlab’ - displays the dashboard in jupyterlab pane.
‘external’ - displays the dashboard in a separate tab. (use in Colab)

dashboard_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the ExplainerDashboard class.

run_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the run method of ExplainerDashboard.

**kwargs:

Additional keyword arguments to pass to the ClassifierExplainer or RegressionExplainer class.

Returns: ExplainerDashboard

pycaret.regression.setup(data: Optional[Union[dict, list, tuple, ndarray, spmatrix, DataFrame]] = None, data_func: Optional[Callable[[], Union[dict, list, tuple, ndarray, spmatrix, DataFrame]]] = None, target: Union[int, str, list, tuple, ndarray, Series] = -1, index: Union[bool, int, str, list, tuple, ndarray, Series] = True, train_size: float = 0.7, test_data: Optional[Union[dict, list, tuple, ndarray, spmatrix, DataFrame]] = None, ordinal_features: Optional[Dict[str, list]] = None, numeric_features: Optional[List[str]] = None, categorical_features: Optional[List[str]] = None, date_features: Optional[List[str]] = None, text_features: Optional[List[str]] = None, ignore_features: Optional[List[str]] = None, keep_features: Optional[List[str]] = None, preprocess: bool = True, create_date_columns: List[str] = ['day', 'month', 'year'], imputation_type: Optional[str] = 'simple', numeric_imputation: Union[int, float, str] = 'mean', categorical_imputation: str = 'mode', iterative_imputation_iters: int = 5, numeric_iterative_imputer: Union[str, Any] = 'lightgbm', categorical_iterative_imputer: Union[str, Any] = 'lightgbm', text_features_method: str = 'tf-idf', max_encoding_ohe: int = 25, encoding_method: Optional[Any] = None, rare_to_value: Optional[float] = None, rare_value: str = 'rare', polynomial_features: bool = False, polynomial_degree: int = 2, low_variance_threshold: Optional[float] = None, group_features: Optional[list] = None, group_names: Optional[Union[str, list]] = None, drop_groups: bool = False, remove_multicollinearity: bool = False, multicollinearity_threshold: float = 0.9, bin_numeric_features: Optional[List[str]] = None, remove_outliers: bool = False, outliers_method: str = 'iforest', outliers_threshold: float = 0.05, transformation: bool = False, transformation_method: str = 'yeo-johnson', normalize: bool = False, normalize_method: str = 'zscore', pca: bool = False, pca_method: str = 'linear', pca_components: Optional[Union[int, float, str]] = None, feature_selection: bool = False, feature_selection_method: str = 'classic', feature_selection_estimator: Union[str, Any] = 'lightgbm', n_features_to_select: Union[int, float] = 0.2, transform_target: bool = False, transform_target_method: str = 'yeo-johnson', custom_pipeline: Optional[Any] = None, custom_pipeline_position: int = -1, data_split_shuffle: bool = True, data_split_stratify: Union[bool, List[str]] = False, fold_strategy: Union[str, Any] = 'kfold', fold: int = 10, fold_shuffle: bool = False, fold_groups: Optional[Union[str, DataFrame]] = None, n_jobs: Optional[int] = -1, use_gpu: bool = False, html: bool = True, session_id: Optional[int] = None, system_log: Union[bool, str, Logger] = True, log_experiment: Union[bool, str, BaseLogger, List[Union[str, BaseLogger]]] = False, experiment_name: Optional[str] = None, experiment_custom_tags: Optional[Dict[str, Any]] = None, log_plots: Union[bool, list] = False, log_profile: bool = False, log_data: bool = False, verbose: bool = True, memory: Union[bool, str, Memory] = True, profile: bool = False, profile_kwargs: Optional[Dict[str, Any]] = None)

This function initializes the training environment and creates the transformation pipeline. Setup function must be called before executing any other function. It takes two mandatory parameters: data and target. All the other parameters are optional.

Example

>>> from pycaret.datasets import get_data
>>> juice = get_data('juice')
>>> from pycaret.classification import *
>>> exp_name = setup(data = juice,  target = 'Purchase')

data: dataframe-like = None

Data set with shape (n_samples, n_features), where n_samples is the number of samples and n_features is the number of features. If data is not a pandas dataframe, it’s converted to one using default column names.

data_func: Callable[[], DATAFRAME_LIKE] = None

The function that generate data (the dataframe-like input). This is useful when the dataset is large, and you need parallel operations such as compare_models. It can avoid broadcasting large dataset from driver to workers. Notice one and only one of data and data_func must be set.

target: int, str or sequence, default = -1

If int or str, respectivcely index or name of the target column in data. The default value selects the last column in the dataset. If sequence, it should have shape (n_samples,). The target can be either binary or multiclass.

index: bool, int, str or sequence, default = True

Handle indices in the data dataframe.

If False: Reset to RangeIndex.
If True: Keep the provided index.
If int: Position of the column to use as index.
If str: Name of the column to use as index.
If sequence: Array with shape=(n_samples,) to use as index.

train_size: float, default = 0.7

Proportion of the dataset to be used for training and validation. Should be between 0.0 and 1.0.

test_data: dataframe-like or None, default = None

If not None, test_data is used as a hold-out set and train_size parameter is ignored. The columns of data and test_data must match.

ordinal_features: dict, default = None

Categorical features to be encoded ordinally. For example, a categorical feature with ‘low’, ‘medium’, ‘high’ values where low < medium < high can be passed as ordinal_features = {‘column_name’ : [‘low’, ‘medium’, ‘high’]}.

numeric_features: list of str, default = None

If the inferred data types are not correct, the numeric_features param can be used to define the data types. It takes a list of strings with column names that are numeric.

categorical_features: list of str, default = None

If the inferred data types are not correct, the categorical_features param can be used to define the data types. It takes a list of strings with column names that are categorical.

date_features: list of str, default = None

If the inferred data types are not correct, the date_features param can be used to overwrite the data types. It takes a list of strings with column names that are DateTime.

text_features: list of str, default = None

Column names that contain a text corpus. If None, no text features are selected.

ignore_features: list of str, default = None

ignore_features param can be used to ignore features during preprocessing and model training. It takes a list of strings with column names that are to be ignored.

keep_features: list of str, default = None

keep_features param can be used to always keep specific features during preprocessing, i.e. these features are never dropped by any kind of feature selection. It takes a list of strings with column names that are to be kept.

preprocess: bool, default = True

When set to False, no transformations are applied except for train_test_split and custom transformations passed in custom_pipeline param. Data must be ready for modeling (no missing values, no dates, categorical data encoding), when preprocess is set to False.

create_date_columns: list of str, default = [“day”, “month”, “year”]

Columns to create from the date features. Note that created features with zero variance (e.g. the feature hour in a column that only contains dates) are ignored. Allowed values are datetime attributes from pandas.Series.dt. The datetime format of the feature is inferred automatically from the first non NaN value.

imputation_type: str or None, default = ‘simple’

The type of imputation to use. Can be either ‘simple’ or ‘iterative’. If None, no imputation of missing values is performed.

numeric_imputation: int, float or str, default = ‘mean’

Imputing strategy for numerical columns. Ignored when imputation_type= iterative. Choose from:

“drop”: Drop rows containing missing values.

“mean”: Impute with mean of column.

“median”: Impute with median of column.

“mode”: Impute with most frequent value.

“knn”: Impute using a K-Nearest Neighbors approach.

int or float: Impute with provided numerical value.

categorical_imputation: str, default = ‘mode’

Imputing strategy for categorical columns. Ignored when imputation_type= iterative. Choose from:

“drop”: Drop rows containing missing values.

“mode”: Impute with most frequent value.

str: Impute with provided string.

iterative_imputation_iters: int, default = 5

Number of iterations. Ignored when imputation_type=simple.

numeric_iterative_imputer: str or sklearn estimator, default = ‘lightgbm’

Regressor for iterative imputation of missing values in numeric features. If None, it uses LGBClassifier. Ignored when imputation_type=simple.

categorical_iterative_imputer: str or sklearn estimator, default = ‘lightgbm’

Regressor for iterative imputation of missing values in categorical features. If None, it uses LGBClassifier. Ignored when imputation_type=simple.

text_features_method: str, default = “tf-idf”

Method with which to embed the text features in the dataset. Choose between “bow” (Bag of Words - CountVectorizer) or “tf-idf” (TfidfVectorizer). Be aware that the sparse matrix output of the transformer is converted internally to its full array. This can cause memory issues for large text embeddings.

max_encoding_ohe: int, default = 25

Categorical columns with max_encoding_ohe or less unique values are encoded using OneHotEncoding. If more, the encoding_method estimator is used. Note that columns with exactly two classes are always encoded ordinally. Set to below 0 to always use OneHotEncoding.

encoding_method: category-encoders estimator, default = None

A category-encoders estimator to encode the categorical columns with more than max_encoding_ohe unique values. If None, category_encoders.target_encoder.TargetEncoder is used.

rare_to_value: float or None, default=None

Minimum fraction of category occurrences in a categorical column. If a category is less frequent than rare_to_value * len(X), it is replaced with the string in rare_value. Use this parameter to group rare categories before encoding the column. If None, ignores this step.

rare_value: str, default=”rare”

Value with which to replace rare categories. Ignored when rare_to_value is None.

polynomial_features: bool, default = False

When set to True, new features are derived using existing numeric features.

polynomial_degree: int, default = 2

Degree of polynomial features. For example, if an input sample is two dimensional and of the form [a, b], the polynomial features with degree = 2 are: [1, a, b, a^2, ab, b^2]. Ignored when polynomial_features is not True.

low_variance_threshold: float or None, default = None

Remove features with a training-set variance lower than the provided threshold. If 0, keep all features with non-zero variance, i.e. remove the features that have the same value in all samples. If None, skip this transformation step.

group_features: list, list of lists or None, default = None

When the dataset contains features with related characteristics, add new fetaures with the following statistical properties of that group: min, max, mean, std, median and mode. The parameter takes a list of feature names or a list of lists of feature names to specify multiple groups.

group_names: str, list, or None, default = None

Group names to be used when naming the new features. The length should match with the number of groups specified in group_features. If None, new features are named using the default form, e.g. group_1, group_2, etc… Ignored when group_features is None.

drop_groups: bool, default=False

Whether to drop the original features in the group. Ignored when group_features is None.

remove_multicollinearity: bool, default = False

When set to True, features with the inter-correlations higher than the defined threshold are removed. For each group, it removes all except the feature with the highest correlation to y.

multicollinearity_threshold: float, default = 0.9

Minimum absolute Pearson correlation to identify correlated features. The default value removes equal columns. Ignored when remove_multicollinearity is not True.

bin_numeric_features: list of str, default = None

To convert numeric features into categorical, bin_numeric_features parameter can be used. It takes a list of strings with column names to be discretized. It does so by using ‘sturges’ rule to determine the number of clusters and then apply KMeans algorithm. Original values of the feature are then replaced by the cluster label.

remove_outliers: bool, default = False

When set to True, outliers from the training data are removed using an Isolation Forest.

outliers_method: str, default = “iforest”

Method with which to remove outliers. Ignored when remove_outliers=False. Possible values are:

‘iforest’: Uses sklearn’s IsolationForest.

‘ee’: Uses sklearn’s EllipticEnvelope.

‘lof’: Uses sklearn’s LocalOutlierFactor.

outliers_threshold: float, default = 0.05

The percentage of outliers to be removed from the dataset. Ignored when remove_outliers=False.

transformation: bool, default = False

When set to True, it applies the power transform to make data more Gaussian-like. Type of transformation is defined by the transformation_method parameter.

transformation_method: str, default = ‘yeo-johnson’

Defines the method for transformation. By default, the transformation method is set to ‘yeo-johnson’. The other available option for transformation is ‘quantile’. Ignored when transformation is not True.

normalize: bool, default = False

When set to True, it transforms the features by scaling them to a given range. Type of scaling is defined by the normalize_method parameter.

normalize_method: str, default = ‘zscore’

Defines the method for scaling. By default, normalize method is set to ‘zscore’ The standard zscore is calculated as z = (x - u) / s. Ignored when normalize is not True. The other options are:

minmax: scales and translates each feature individually such that it is in the range of 0 - 1.
maxabs: scales and translates each feature individually such that the maximal absolute value of each feature will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity.
robust: scales and translates each feature according to the Interquartile range. When the dataset contains outliers, robust scaler often gives better results.

pca: bool, default = False

When set to True, dimensionality reduction is applied to project the data into a lower dimensional space using the method defined in pca_method parameter.

pca_method: str, default = ‘linear’

Method with which to apply PCA. Possible values are:

‘linear’: Uses Singular Value Decomposition.
‘kernel’: Dimensionality reduction through the use of RBF kernel.
‘incremental’: Similar to ‘linear’, but more efficient for large datasets.

pca_components: int, float, str or None, default = None

Number of components to keep. This parameter is ignored when pca=False.

If None: All components are kept.
If int: Absolute number of components.
If float: Such an amount that the variance that needs to be explained
is greater than the percentage specified by n_components. Value should lie between 0 and 1 (ony for pca_method=’linear’).
If “mle”: Minka’s MLE is used to guess the dimension (ony for pca_method=’linear’).

feature_selection: bool, default = False

When set to True, a subset of features is selected based on a feature importance score determined by feature_selection_estimator.

feature_selection_method: str, default = ‘classic’

Algorithm for feature selection. Choose from:

‘univariate’: Uses sklearn’s SelectKBest.
‘classic’: Uses sklearn’s SelectFromModel.
‘sequential’: Uses sklearn’s SequentialFeatureSelector.

feature_selection_estimator: str or sklearn estimator, default = ‘lightgbm’

Classifier used to determine the feature importances. The estimator should have a feature_importances_ or coef_ attribute after fitting. If None, it uses LGBRegressor. This parameter is ignored when feature_selection_method=univariate.

n_features_to_select: int or float, default = 0.2

The maximum number of features to select with feature_selection. If <1, it’s the fraction of starting features. Note that this parameter doesn’t take features in ignore_features or keep_features into account when counting.

transform_target: bool, default = False

When set to True, target variable is transformed using the method defined in transform_target_method param. Target transformation is applied separately from feature transformations.

transform_target_method: str, default = ‘yeo-johnson’

Defines the method for transformation. By default, the transformation method is set to ‘yeo-johnson’. The other available option for transformation is ‘quantile’. Ignored when transform_target is not True.

custom_pipeline: list of (str, transformer), dict or Pipeline, default = None

Addidiotnal custom transformers. If passed, they are applied to the pipeline last, after all the build-in transformers.

custom_pipeline_position: int, default = -1

Position of the custom pipeline in the overal preprocessing pipeline. The default value adds the custom pipeline last.

data_split_shuffle: bool, default = True

When set to False, prevents shuffling of rows during ‘train_test_split’.

data_split_stratify: bool or list, default = False

Controls stratification during ‘train_test_split’. When set to True, will stratify by target column. To stratify on any other columns, pass a list of column names. Ignored when data_split_shuffle is False.

fold_strategy: str or sklearn CV generator object, default = ‘kfold’

Choice of cross validation strategy. Possible values are:

‘kfold’
‘groupkfold’
‘timeseries’
a custom CV generator object compatible with scikit-learn.

For groupkfold, column name must be passed in fold_groups parameter. Example: setup(fold_strategy="groupkfold", fold_groups="COLUMN_NAME")

fold: int, default = 10

Number of folds to be used in cross validation. Must be at least 2. This is a global setting that can be over-written at function level by using fold parameter. Ignored when fold_strategy is a custom object.

fold_shuffle: bool, default = False

Controls the shuffle parameter of CV. Only applicable when fold_strategy is ‘kfold’ or ‘stratifiedkfold’. Ignored when fold_strategy is a custom object.

fold_groups: str or array-like, with shape (n_samples,), default = None

Optional group labels when ‘GroupKFold’ is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.

n_jobs: int, default = -1

The number of jobs to run in parallel (for functions that supports parallel processing) -1 means using all processors. To run all functions on single processor set n_jobs to None.

use_gpu: bool or str, default = False

When set to True, it will use GPU for training with algorithms that support it, and fall back to CPU if they are unavailable. When set to ‘force’, it will only use GPU-enabled algorithms and raise exceptions when they are unavailable. When False, all algorithms are trained using CPU only.

GPU enabled algorithms:

Extreme Gradient Boosting, requires no further installation
CatBoost Classifier, requires no further installation (GPU is only enabled when data > 50,000 rows)
Light Gradient Boosting Machine, requires GPU installation https://lightgbm.readthedocs.io/en/latest/GPU-Tutorial.html
Linear Regression, Lasso Regression, Ridge Regression, K Neighbors Regressor, Random Forest, Support Vector Regression, Elastic Net requires cuML >= 0.15 https://github.com/rapidsai/cuml

html: bool, default = True

When set to False, prevents runtime display of monitor. This must be set to False when the environment does not support IPython. For example, command line terminal, Databricks Notebook, Spyder and other similar IDEs.

session_id: int, default = None

Controls the randomness of experiment. It is equivalent to ‘random_state’ in scikit-learn. When None, a pseudo random number is generated. This can be used for later reproducibility of the entire experiment.

log_experiment: bool, default = False

A (list of) PyCaret BaseLogger or str (one of ‘mlflow’, ‘wandb’, ‘comet_ml’) corresponding to a logger to determine which experiment loggers to use. Setting to True will use just MLFlow. If wandb (Weights & Biases) or comet_ml is installed, will also log there.

system_log: bool or str or logging.Logger, default = True

Whether to save the system logging file (as logs.log). If the input is a string, use that as the path to the logging file. If the input already is a logger object, use that one instead.

experiment_name: str, default = None

Name of the experiment for logging. Ignored when log_experiment is False.

experiment_custom_tags: dict, default = None

Dictionary of tag_name: String -> value: (String, but will be string-ified if not) passed to the mlflow.set_tags to add new custom tags for the experiment.

log_plots: bool or list, default = False

When set to True, certain plots are logged automatically in the MLFlow server. To change the type of plots to be logged, pass a list containing plot IDs. Refer to documentation of plot_model. Ignored when log_experiment is False.

log_profile: bool, default = False

When set to True, data profile is logged on the MLflow server as a html file. Ignored when log_experiment is False.

log_data: bool, default = False

When set to True, dataset is logged on the MLflow server as a csv file. Ignored when log_experiment is False.

verbose: bool, default = True

When set to False, Information grid is not printed.

memory: str, bool or Memory, default=True

Used to cache the fitted transformers of the pipeline.: If False: No caching is performed. If True: A default temp directory is used. If str: Path to the caching directory.

profile: bool, default = False

When set to True, an interactive EDA report is displayed.

profile_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the ProfileReport method used to create the EDA report. Ignored if profile is False.

Returns: Global variables that can be changed using the set_config function.

pycaret.regression.create_model(estimator: Union[str, Any], fold: Optional[Union[int, Any]] = None, round: int = 4, cross_validation: bool = True, fit_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, experiment_custom_tags: Optional[Dict[str, Any]] = None, engine: Optional[str] = None, verbose: bool = True, return_train_score: bool = False, **kwargs)

This function trains and evaluates the performance of a given estimator using cross validation. The output of this function is a score grid with CV scores by fold. Metrics evaluated during CV can be accessed using the get_metrics function. Custom metrics can be added or removed using add_metric and remove_metric function. All the available models can be accessed using the models function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')

estimator: str or scikit-learn compatible object

ID of an estimator available in model library or pass an untrained model object consistent with scikit-learn API. Estimators available in the model library (ID - Name):

‘lr’ - Linear Regression
‘lasso’ - Lasso Regression
‘ridge’ - Ridge Regression
‘en’ - Elastic Net
‘lar’ - Least Angle Regression
‘llar’ - Lasso Least Angle Regression
‘omp’ - Orthogonal Matching Pursuit
‘br’ - Bayesian Ridge
‘ard’ - Automatic Relevance Determination
‘par’ - Passive Aggressive Regressor
‘ransac’ - Random Sample Consensus
‘tr’ - TheilSen Regressor
‘huber’ - Huber Regressor
‘kr’ - Kernel Ridge
‘svm’ - Support Vector Regression
‘knn’ - K Neighbors Regressor
‘dt’ - Decision Tree Regressor
‘rf’ - Random Forest Regressor
‘et’ - Extra Trees Regressor
‘ada’ - AdaBoost Regressor
‘gbr’ - Gradient Boosting Regressor
‘mlp’ - MLP Regressor
‘xgboost’ - Extreme Gradient Boosting
‘lightgbm’ - Light Gradient Boosting Machine
‘catboost’ - CatBoost Regressor

fold: int or scikit-learn compatible CV generator, default = None

Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.

round: int, default = 4

Number of decimal places the metrics in the score grid will be rounded to.

cross_validation: bool, default = True

When set to False, metrics are evaluated on holdout set. fold param is ignored when cross_validation is set to False.

fit_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the fit method of the model.

groups: str or array-like, with shape (n_samples,), default = None

Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.

experiment_custom_tags: dict, default = None

Dictionary of tag_name: String -> value: (String, but will be string-ified if not) passed to the mlflow.set_tags to add new custom tags for the experiment.

engine: Optional[str] = None

The execution engine to use for the model, e.g. for Linear Regression (“lr”), users can switch between “sklearn” and “sklearnex” by specifying engine=”sklearnex”.

verbose: bool, default = True

Score grid is not printed when verbose is set to False.

return_train_score: bool, default = False

If False, returns the CV Validation scores only. If True, returns the CV training scores along with the CV validation scores. This is useful when the user wants to do bias-variance tradeoff. A high CV training score with a low corresponding CV validation score indicates overfitting.

**kwargs:

Additional keyword arguments to pass to the estimator.

Returns: Trained Model

Warning

Models are not logged on the MLFlow server when cross_validation param is set to False.

pycaret.regression.compare_models(include: Optional[List[Union[str, Any]]] = None, exclude: Optional[List[str]] = None, fold: Optional[Union[int, Any]] = None, round: int = 4, cross_validation: bool = True, sort: str = 'R2', n_select: int = 1, budget_time: Optional[float] = None, turbo: bool = True, errors: str = 'ignore', fit_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, experiment_custom_tags: Optional[Dict[str, Any]] = None, engine: Optional[Dict[str, str]] = None, verbose: bool = True, parallel: Optional[ParallelBackend] = None)

This function trains and evaluates performance of all estimators available in the model library using cross validation. The output of this function is a score grid with average cross validated scores. Metrics evaluated during CV can be accessed using the get_metrics function. Custom metrics can be added or removed using add_metric and remove_metric function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> best_model = compare_models()

include: list of str or scikit-learn compatible object, default = None: To train and evaluate select models, list containing model ID or scikit-learn compatible object can be passed in include param. To see a list of all models available in the model library use the models function.
exclude: list of str, default = None: To omit certain models from training and evaluation, pass a list containing model id in the exclude parameter. To see a list of all models available in the model library use the models function.
fold: int or scikit-learn compatible CV generator, default = None: Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.
round: int, default = 4: Number of decimal places the metrics in the score grid will be rounded to.
cross_validation: bool, default = True: When set to False, metrics are evaluated on holdout set. fold param is ignored when cross_validation is set to False.
sort: str, default = ‘R2’: The sort order of the score grid. It also accepts custom metrics that are added through the add_metric function.
n_select: int, default = 1: Number of top_n models to return. For example, to select top 3 models use n_select = 3.
budget_time: int or float, default = None: If not None, will terminate execution of the function after budget_time minutes have passed and return results up to that point.
turbo: bool, default = True: When set to True, it excludes estimators with longer training times. To see which algorithms are excluded use the models function.
errors: str, default = ‘ignore’: When set to ‘ignore’, will skip the model with exceptions and continue. If ‘raise’, will break the function when exceptions are raised.
fit_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the fit method of the model.
groups: str or array-like, with shape (n_samples,), default = None: Optional group labels when ‘GroupKFold’ is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.
experiment_custom_tags: dict, default = None: Dictionary of tag_name: String -> value: (String, but will be string-ified if not) passed to the mlflow.set_tags to add new custom tags for the experiment.
engine: Optional[Dict[str, str]] = None: The execution engines to use for the models in the form of a dict of model_id: engine - e.g. for Linear Regression (“lr”, users can switch between “sklearn” and “sklearnex” by specifying engine={“lr”: “sklearnex”}
verbose: bool, default = True: Score grid is not printed when verbose is set to False.
parallel: pycaret.internal.parallel.parallel_backend.ParallelBackend, default = None: A ParallelBackend instance. For example if you have a SparkSession session, you can use FugueBackend(session) to make this function running using Spark. For more details, see FugueBackend

Returns: Trained model or list of trained models, depending on the n_select param.

Warning

Changing turbo parameter to False may result in very high training times with datasets exceeding 10,000 rows.
No models are logged in MLFlow when cross_validation parameter is False.

pycaret.regression.ensemble_model(estimator, method: str = 'Bagging', fold: Optional[Union[int, Any]] = None, n_estimators: int = 10, round: int = 4, choose_better: bool = False, optimize: str = 'R2', fit_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, verbose: bool = True, return_train_score: bool = False) → Any

This function ensembles a given estimator. The output of this function is a score grid with CV scores by fold. Metrics evaluated during CV can be accessed using the get_metrics function. Custom metrics can be added or removed using add_metric and remove_metric function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> dt = create_model('dt')
>>> bagged_dt = ensemble_model(dt, method = 'Bagging')

estimator: scikit-learn compatible object: Trained model object
method: str, default = ‘Bagging’: Method for ensembling base estimator. It can be ‘Bagging’ or ‘Boosting’.
fold: int or scikit-learn compatible CV generator, default = None: Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.
n_estimators: int, default = 10: The number of base estimators in the ensemble. In case of perfect fit, the learning procedure is stopped early.
round: int, default = 4: Number of decimal places the metrics in the score grid will be rounded to.
choose_better: bool, default = False: When set to True, the returned object is always better performing. The metric used for comparison is defined by the optimize parameter.
optimize: str, default = ‘R2’: Metric to compare for model selection when choose_better is True.
fit_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the fit method of the model.
groups: str or array-like, with shape (n_samples,), default = None: Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.
return_train_score: bool, default = False: If False, returns the CV Validation scores only. If True, returns the CV training scores along with the CV validation scores. This is useful when the user wants to do bias-variance tradeoff. A high CV training score with a low corresponding CV validation score indicates overfitting.
verbose: bool, default = True: Score grid is not printed when verbose is set to False.

Returns: Trained Model

pycaret.regression.tune_model(estimator, fold: Optional[Union[int, Any]] = None, round: int = 4, n_iter: int = 10, custom_grid: Optional[Union[Dict[str, list], Any]] = None, optimize: str = 'R2', custom_scorer=None, search_library: str = 'scikit-learn', search_algorithm: Optional[str] = None, early_stopping: Any = False, early_stopping_max_iters: int = 10, choose_better: bool = True, fit_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, return_tuner: bool = False, verbose: bool = True, tuner_verbose: Union[int, bool] = True, return_train_score: bool = False, **kwargs)

This function tunes the hyperparameters of a given estimator. The output of this function is a score grid with CV scores by fold of the best selected model based on optimize parameter. Metrics evaluated during CV can be accessed using the get_metrics function. Custom metrics can be added or removed using add_metric and remove_metric function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> tuned_lr = tune_model(lr)

estimator: scikit-learn compatible object

Trained model object

fold: int or scikit-learn compatible CV generator, default = None

Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.

round: int, default = 4

Number of decimal places the metrics in the score grid will be rounded to.

n_iter: int, default = 10

Number of iterations in the grid search. Increasing ‘n_iter’ may improve model performance but also increases the training time.

custom_grid: dictionary, default = None

To define custom search space for hyperparameters, pass a dictionary with parameter name and values to be iterated. Custom grids must be in a format supported by the defined search_library.

optimize: str, default = ‘R2’

Metric name to be evaluated for hyperparameter tuning. It also accepts custom metrics that are added through the add_metric function.

custom_scorer: object, default = None

custom scoring strategy can be passed to tune hyperparameters of the model. It must be created using sklearn.make_scorer. It is equivalent of adding custom metric using the add_metric function and passing the name of the custom metric in the optimize parameter. Will be deprecated in future.

search_library: str, default = ‘scikit-learn’

The search library used for tuning hyperparameters. Possible values:

‘scikit-learn’ - default, requires no further installation
https://github.com/scikit-learn/scikit-learn
‘scikit-optimize’ - pip install scikit-optimize
https://scikit-optimize.github.io/stable/
‘tune-sklearn’ - pip install tune-sklearn ray[tune]
https://github.com/ray-project/tune-sklearn
‘optuna’ - pip install optuna
https://optuna.org/

search_algorithm: str, default = None

The search algorithm depends on the search_library parameter. Some search algorithms require additional libraries to be installed. If None, will use search library-specific default algorithm.

‘scikit-learn’ possible values:
- ‘random’ : random grid search (default)
- ‘grid’ : grid search
‘scikit-optimize’ possible values:
- ‘bayesian’ : Bayesian search (default)
‘tune-sklearn’ possible values:
- ‘random’ : random grid search (default)
- ‘grid’ : grid search
- ‘bayesian’ : pip install scikit-optimize
- ‘hyperopt’ : pip install hyperopt
- ‘optuna’ : pip install optuna
- ‘bohb’ : pip install hpbandster ConfigSpace
‘optuna’ possible values:
- ‘random’ : randomized search
- ‘tpe’ : Tree-structured Parzen Estimator search (default)

early_stopping: bool or str or object, default = False

Use early stopping to stop fitting to a hyperparameter configuration if it performs poorly. Ignored when search_library is scikit-learn, or if the estimator does not have ‘partial_fit’ attribute. If False or None, early stopping will not be used. Can be either an object accepted by the search library or one of the following:

‘asha’ for Asynchronous Successive Halving Algorithm
‘hyperband’ for Hyperband
‘median’ for Median Stopping Rule
If False or None, early stopping will not be used.

early_stopping_max_iters: int, default = 10

Maximum number of epochs to run for each sampled configuration. Ignored if early_stopping is False or None.

choose_better: bool, default = True

When set to True, the returned object is always better performing. The metric used for comparison is defined by the optimize parameter.

fit_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the fit method of the tuner.

groups: str or array-like, with shape (n_samples,), default = None

Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.

return_tuner: bool, default = False

When set to True, will return a tuple of (model, tuner_object).

verbose: bool, default = True

Score grid is not printed when verbose is set to False.

tuner_verbose: bool or in, default = True

If True or above 0, will print messages from the tuner. Higher values print more messages. Ignored when verbose param is False.

return_train_score: bool, default = False

If False, returns the CV Validation scores only. If True, returns the CV training scores along with the CV validation scores. This is useful when the user wants to do bias-variance tradeoff. A high CV training score with a low corresponding CV validation score indicates overfitting.

**kwargs:

Additional keyword arguments to pass to the optimizer.

Returns: Trained Model and Optional Tuner Object when return_tuner is True.

Warning

Using ‘grid’ as search_algorithm may result in very long computation. Only recommended with smaller search spaces that can be defined in the custom_grid parameter.
search_library ‘tune-sklearn’ does not support GPU models.

pycaret.regression.blend_models(estimator_list: list, fold: Optional[Union[int, Any]] = None, round: int = 4, choose_better: bool = False, optimize: str = 'R2', weights: Optional[List[float]] = None, fit_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, verbose: bool = True, return_train_score: bool = False)

This function trains a Voting Regressor for select models passed in the estimator_list param. The output of this function is a score grid with CV scores by fold. Metrics evaluated during CV can be accessed using the get_metrics function. Custom metrics can be added or removed using add_metric and remove_metric function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> top3 = compare_models(n_select = 3)
>>> blender = blend_models(top3)

estimator_list: list of scikit-learn compatible objects: List of trained model objects
fold: int or scikit-learn compatible CV generator, default = None: Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.
round: int, default = 4: Number of decimal places the metrics in the score grid will be rounded to.
choose_better: bool, default = False: When set to True, the returned object is always better performing. The metric used for comparison is defined by the optimize parameter.
optimize: str, default = ‘R2’: Metric to compare for model selection when choose_better is True.
weights: list, default = None: Sequence of weights (float or int) to weight the occurrences of predicted class labels (hard voting) or class probabilities before averaging (soft voting). Uses uniform weights when None.
fit_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the fit method of the model.
groups: str or array-like, with shape (n_samples,), default = None: Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.
verbose: bool, default = True: Score grid is not printed when verbose is set to False.
return_train_score: bool, default = False: If False, returns the CV Validation scores only. If True, returns the CV training scores along with the CV validation scores. This is useful when the user wants to do bias-variance tradeoff. A high CV training score with a low corresponding CV validation score indicates overfitting.

Returns: Trained Model

pycaret.regression.stack_models(estimator_list: list, meta_model=None, meta_model_fold: Optional[Union[int, Any]] = 5, fold: Optional[Union[int, Any]] = None, round: int = 4, restack: bool = True, choose_better: bool = False, optimize: str = 'R2', fit_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, verbose: bool = True, return_train_score: bool = False)

This function trains a meta model over select estimators passed in the estimator_list parameter. The output of this function is a score grid with CV scores by fold. Metrics evaluated during CV can be accessed using the get_metrics function. Custom metrics can be added or removed using add_metric and remove_metric function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> top3 = compare_models(n_select = 3)
>>> stacker = stack_models(top3)

estimator_list: list of scikit-learn compatible objects: List of trained model objects
meta_model: scikit-learn compatible object, default = None: When None, Linear Regression is trained as a meta model.
meta_model_fold: integer or scikit-learn compatible CV generator, default = 5: Controls internal cross-validation. Can be an integer or a scikit-learn CV generator. If set to an integer, will use (Stratifed)KFold CV with that many folds. See scikit-learn documentation on Stacking for more details.
fold: int or scikit-learn compatible CV generator, default = None: Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.
round: int, default = 4: Number of decimal places the metrics in the score grid will be rounded to.
restack: bool, default = False: When set to False, only the predictions of estimators will be used as training data for the meta_model.
choose_better: bool, default = False: When set to True, the returned object is always better performing. The metric used for comparison is defined by the optimize parameter.
optimize: str, default = ‘R2’: Metric to compare for model selection when choose_better is True.
fit_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the fit method of the model.
groups: str or array-like, with shape (n_samples,), default = None: Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.
verbose: bool, default = True: Score grid is not printed when verbose is set to False.
return_train_score: bool, default = False: If False, returns the CV Validation scores only. If True, returns the CV training scores along with the CV validation scores. This is useful when the user wants to do bias-variance tradeoff. A high CV training score with a low corresponding CV validation score indicates overfitting.

Returns: Trained Model

pycaret.regression.plot_model(estimator, plot: str = 'residuals', scale: float = 1, save: bool = False, fold: Optional[Union[int, Any]] = None, fit_kwargs: Optional[dict] = None, plot_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, use_train_data: bool = False, verbose: bool = True, display_format: Optional[str] = None) → Optional[str]

This function analyzes the performance of a trained model on holdout set. It may require re-training the model in certain cases.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> plot_model(lr, plot = 'residual')

estimator: scikit-learn compatible object

Trained model object

plot: str, default = ‘residual’

List of available plots (ID - Name):

‘pipeline’ - Schematic drawing of the preprocessing pipeline
‘residuals_interactive’ - Interactive Residual plots
‘residuals’ - Residuals Plot
‘error’ - Prediction Error Plot
‘cooks’ - Cooks Distance Plot
‘rfe’ - Recursive Feat. Selection
‘learning’ - Learning Curve
‘vc’ - Validation Curve
‘manifold’ - Manifold Learning
‘feature’ - Feature Importance
‘feature_all’ - Feature Importance (All)
‘parameter’ - Model Hyperparameter
‘tree’ - Decision Tree

scale: float, default = 1

The resolution scale of the figure.

save: bool, default = False

When set to True, plot is saved in the current working directory.

fold: int or scikit-learn compatible CV generator, default = None

Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.

fit_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the fit method of the model.

plot_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the visualizer class.

pipeline: fontsize -> int

plot_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the visualizer class.

pipeline: fontsize -> int

groups: str or array-like, with shape (n_samples,), default = None

Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.

use_train_data: bool, default = False

When set to true, train data will be used for plots, instead of test data.

verbose: bool, default = True

When set to False, progress bar is not displayed.

display_format: str, default = None

To display plots in Streamlit (https://www.streamlit.io/), set this to ‘streamlit’. Currently, not all plots are supported.

Returns: Path to saved file, if any.

pycaret.regression.evaluate_model(estimator, fold: Optional[Union[int, Any]] = None, fit_kwargs: Optional[dict] = None, plot_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, use_train_data: bool = False)

This function displays a user interface for analyzing performance of a trained model. It calls the plot_model function internally.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> evaluate_model(lr)

estimator: scikit-learn compatible object: Trained model object
fold: int or scikit-learn compatible CV generator, default = None: Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.
fit_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the fit method of the model.
plot_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the visualizer class.
groups: str or array-like, with shape (n_samples,), default = None: Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.
use_train_data: bool, default = False: When set to true, train data will be used for plots, instead of test data.

Returns: None

Warning

This function only works in IPython enabled Notebook.

pycaret.regression.interpret_model(estimator, plot: str = 'summary', feature: Optional[str] = None, observation: Optional[int] = None, use_train_data: bool = False, X_new_sample: Optional[DataFrame] = None, y_new_sample: Optional[DataFrame] = None, save: Union[str, bool] = False, **kwargs)

This function takes a trained model object and returns an interpretation plot based on the test / hold-out set.

This function is implemented based on the SHAP (SHapley Additive exPlanations), which is a unified approach to explain the output of any machine learning model. SHAP connects game theory with local explanations.

For more information: https://shap.readthedocs.io/en/latest/

For more information on Partial Dependence Plot: https://github.com/SauceCat/PDPbox

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp = setup(data = boston,  target = 'medv')
>>> xgboost = create_model('xgboost')
>>> interpret_model(xgboost)

estimator: scikit-learn compatible object

Trained model object

plotstr, default = ‘summary’

Abbreviation of type of plot. The current list of plots supported are (Plot - Name):

‘summary’ - Summary Plot using SHAP
‘correlation’ - Dependence Plot using SHAP
‘reason’ - Force Plot using SHAP
‘pdp’ - Partial Dependence Plot
‘msa’ - Morris Sensitivity Analysis
‘pfi’ - Permutation Feature Importance

feature: str, default = None

This parameter is only needed when plot = ‘correlation’ or ‘pdp’. By default feature is set to None which means the first column of the dataset will be used as a variable. A feature parameter must be passed to change this.

observation: integer, default = None

This parameter only comes into effect when plot is set to ‘reason’. If no observation number is provided, it will return an analysis of all observations with the option to select the feature on x and y axes through drop down interactivity. For analysis at the sample level, an observation parameter must be passed with the index value of the observation in test / hold-out set.

use_train_data: bool, default = False

When set to true, train data will be used for plots, instead of test data.

X_new_sample: pd.DataFrame, default = None

Row from an out-of-sample dataframe (neither train nor test data) to be plotted. The sample must have the same columns as the raw input train data, and it is transformed by the preprocessing pipeline automatically before plotting.

y_new_sample: pd.DataFrame, default = None

Row from an out-of-sample dataframe (neither train nor test data) to be plotted. The sample must have the same columns as the raw input label data, and it is transformed by the preprocessing pipeline automatically before plotting.

save: string or bool, default = False

When set to True, Plot is saved as a ‘png’ file in current working directory. When a path destination is given, Plot is saved as a ‘png’ file the given path to the directory of choice.

**kwargs:

Additional keyword arguments to pass to the plot.

Returns: None

pycaret.regression.predict_model(estimator, data: Optional[DataFrame] = None, round: int = 4, verbose: bool = True) → DataFrame

This function predicts Label using a trained model. When data is None, it predicts label on the holdout set.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> pred_holdout = predict_model(lr)
>>> pred_unseen = predict_model(lr, data = unseen_dataframe)

estimator: scikit-learn compatible object: Trained model object
datapandas.DataFrame: Shape (n_samples, n_features). All features used during training must be available in the unseen dataset.
round: int, default = 4: Number of decimal places to round predictions to.
verbose: bool, default = True: When set to False, holdout score grid is not printed.

Returns: pandas.DataFrame

Warning

The behavior of the predict_model is changed in version 2.1 without backward compatibility. As such, the pipelines trained using the version (<= 2.0), may not work for inference with version >= 2.1. You can either retrain your models with a newer version or downgrade the version for inference.

pycaret.regression.finalize_model(estimator, fit_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, model_only: bool = False, experiment_custom_tags: Optional[Dict[str, Any]] = None) → Any

This function trains a given estimator on the entire dataset including the holdout set.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> final_lr = finalize_model(lr)

estimator: scikit-learn compatible object: Trained model object
fit_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the fit method of the model.
groups: str or array-like, with shape (n_samples,), default = None: Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels.
model_onlybool, default = False: Whether to return the complete fitted pipeline or only the fitted model.
experiment_custom_tags: dict, default = None: Dictionary of tag_name: String -> value: (String, but will be string-ified if not) passed to the mlflow.set_tags to add new custom tags for the experiment.

Returns: Trained pipeline or model object fitted on complete dataset.

pycaret.regression.deep_check(estimator, check_kwargs: Optional[dict] = None) → None

This function runs a full suite check over a trained model using deepchecks library.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> deep_check(lr)

estimator: scikit-learn compatible object: Trained model object
check_kwargs: dict, default = {} (empty dict): arguments to be passed to deepchecks full_suite class.

Returns: Results of deepchecks.suites.full_suite.run

pycaret.regression.deploy_model(model, model_name: str, authentication: dict, platform: str = 'aws')

This function deploys the transformation pipeline and trained model on cloud.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> # sets appropriate credentials for the platform as environment variables
>>> import os
>>> os.environ["AWS_ACCESS_KEY_ID"] = str("foo")
>>> os.environ["AWS_SECRET_ACCESS_KEY"] = str("bar")
>>> deploy_model(model = lr, model_name = 'lr-for-deployment', platform = 'aws', authentication = {'bucket' : 'S3-bucket-name'})

Amazon Web Service (AWS) users:

To deploy a model on AWS S3 (‘aws’), the credentials have to be passed. The easiest way is to use environment variables in your local environment. Following information from the IAM portal of amazon console account are required:

AWS Access Key ID
AWS Secret Key Access

More info: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#environment-variables

Google Cloud Platform (GCP) users:

To deploy a model on Google Cloud Platform (‘gcp’), project must be created using command line or GCP console. Once project is created, you must create a service account and download the service account key as a JSON file to set environment variables in your local environment.

More info: https://cloud.google.com/docs/authentication/production

Microsoft Azure (Azure) users:

To deploy a model on Microsoft Azure (‘azure’), environment variables for connection string must be set in your local environment. Go to settings of storage account on Azure portal to access the connection string required.

AZURE_STORAGE_CONNECTION_STRING (required as environment variable)

More info: https://docs.microsoft.com/en-us/azure/storage/blobs/storage-quickstart-blobs-python?toc=%2Fpython%2Fazure%2FTOC.json

model: scikit-learn compatible object

Trained model object

model_name: str

Name of model.

authentication: dict

Dictionary of applicable authentication tokens.

When platform = ‘aws’: {‘bucket’ : ‘S3-bucket-name’, ‘path’: (optional) folder name under the bucket}

When platform = ‘gcp’: {‘project’: ‘gcp-project-name’, ‘bucket’ : ‘gcp-bucket-name’}

When platform = ‘azure’: {‘container’: ‘azure-container-name’}

platform: str, default = ‘aws’

Name of the platform. Currently supported platforms: ‘aws’, ‘gcp’ and ‘azure’.

Returns: None

pycaret.regression.save_model(model, model_name: str, model_only: bool = False, verbose: bool = True, **kwargs)

This function saves the transformation pipeline and trained model object into the current working directory as a pickle file for later use.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> save_model(lr, 'saved_lr_model')

model: scikit-learn compatible object: Trained model object
model_name: str: Name of the model.
model_only: bool, default = False: When set to True, only trained model object is saved instead of the entire pipeline.
**kwargs:: Additional keyword arguments to pass to joblib.dump().
verbose: bool, default = True: Success message is not printed when verbose is set to False.

Returns: Tuple of the model object and the filename.

pycaret.regression.load_model(model_name: str, platform: Optional[str] = None, authentication: Optional[Dict[str, str]] = None, verbose: bool = True)

This function loads a previously saved pipeline.

Example

>>> from pycaret.regression import load_model
>>> saved_lr = load_model('saved_lr_model')

model_name: str

Name of the model.

platform: str, default = None

Name of the cloud platform. Currently supported platforms: ‘aws’, ‘gcp’ and ‘azure’.

authentication: dict, default = None

dictionary of applicable authentication tokens.

when platform = ‘aws’: {‘bucket’ : ‘Name of Bucket on S3’, ‘path’: (optional) folder name under the bucket}

when platform = ‘gcp’: {‘project’: ‘gcp-project-name’, ‘bucket’ : ‘gcp-bucket-name’}

when platform = ‘azure’: {‘container’: ‘azure-container-name’}

verbose: bool, default = True

Success message is not printed when verbose is set to False.

Returns: Trained Model

pycaret.regression.automl(optimize: str = 'R2', use_holdout: bool = False, turbo: bool = True, return_train_score: bool = False) → Any

This function returns the best model out of all trained models in current session based on the optimize parameter. Metrics evaluated can be accessed using the get_metrics function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> top3 = compare_models(n_select = 3)
>>> tuned_top3 = [tune_model(i) for i in top3]
>>> blender = blend_models(tuned_top3)
>>> stacker = stack_models(tuned_top3)
>>> best_mae_model = automl(optimize = 'MAE')

optimize: str, default = ‘R2’: Metric to use for model selection. It also accepts custom metrics added using the add_metric function.
use_holdout: bool, default = False: When set to True, metrics are evaluated on holdout set instead of CV.
turbo: bool, default = True: When set to True and use_holdout is False, only models created with default fold parameter will be considered. If set to False, models created with a non-default fold parameter will be scored again using default fold settings, so that they can be compared.
return_train_score: bool, default = False: If False, returns the CV Validation scores only. If True, returns the CV training scores along with the CV validation scores. This is useful when the user wants to do bias-variance tradeoff. A high CV training score with a low corresponding CV validation score indicates overfitting.

Returns: Trained Model

pycaret.regression.pull(pop: bool = False) → DataFrame

Returns the latest displayed table.

Parameters: pop (bool, default = False) – If true, will pop (remove) the returned dataframe from the display container.
Return type: pandas.DataFrame

pycaret.regression.models(type: Optional[str] = None, internal: bool = False, raise_errors: bool = True) → DataFrame

Returns table of models available in the model library.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> all_models = models()

type: str, default = None

linear : filters and only return linear models
tree : filters and only return tree based models
ensemble : filters and only return ensemble models

internal: bool, default = False

When True, will return extra columns and rows used internally.

raise_errors: bool, default = True

When False, will suppress all exceptions, ignoring models that couldn’t be created.

Returns: pandas.DataFrame

pycaret.regression.get_metrics(reset: bool = False, include_custom: bool = True, raise_errors: bool = True) → DataFrame

Returns table of available metrics used in the experiment.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> all_metrics = get_metrics()

reset: bool, default = False: When True, will reset all changes made using the add_metric and remove_metric function.
include_custom: bool, default = True: Whether to include user added (custom) metrics or not.
raise_errors: bool, default = True: If False, will suppress all exceptions, ignoring models that couldn’t be created.

Returns: pandas.DataFrame

pycaret.regression.add_metric(id: str, name: str, score_func: type, greater_is_better: bool = True, **kwargs) → Series

Adds a custom metric to be used in the experiment.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> from sklearn.metrics import explained_variance_score
>>> add_metric('evs', 'EVS', explained_variance_score)

id: str: Unique id for the metric.
name: str: Display name of the metric.
score_func: type: Score function (or loss function) with signature score_func(y, y_pred, **kwargs).
greater_is_better: bool, default = True: Whether score_func is higher the better or not.
**kwargs:: Arguments to be passed to score function.

Returns: pandas.Series

pycaret.regression.remove_metric(name_or_id: str)

Removes a metric from experiment.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'mredv')
>>> remove_metric('MAPE')

name_or_id: str: Display name or ID of the metric.

Returns: None

pycaret.regression.get_logs(experiment_name: Optional[str] = None, save: bool = False) → DataFrame

Returns a table of experiment logs. Only works when log_experiment is True when initializing the setup function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv', log_experiment = True)
>>> best = compare_models()
>>> exp_logs = get_logs()

experiment_name: str, default = None: When None current active run is used.
save: bool, default = False: When set to True, csv file is saved in current working directory.

Returns: pandas.DataFrame

pycaret.regression.get_config(variable: Optional[str] = None)

This function is used to access global environment variables.

Example

>>> X_train = get_config('X_train')

This will return training features.

variablestr, default = None: Name of the variable to return the value of. If None, will return a list of possible names.

Return type: variable

pycaret.regression.set_config(variable: str, value)

This function is used to reset global environment variables.

Example

>>> set_config('seed', 123)

This will set the global seed to ‘123’.

pycaret.regression.save_experiment(path_or_file: Union[str, PathLike, BinaryIO], **cloudpickle_kwargs) → None

Saves the experiment to a pickle file.

The experiment is saved using cloudpickle to deal with lambda functions. The data or test data is NOT saved with the experiment and will need to be specified again when loading using load_experiment.

path_or_file: str or BinaryIO (file pointer): The path/file pointer to save the experiment to.
**cloudpickle_kwargs:: Kwargs to pass to the cloudpickle.dump call.

Returns: None

pycaret.regression.load_experiment(path_or_file: Union[str, PathLike, BinaryIO], data: Optional[Union[dict, list, tuple, ndarray, spmatrix, DataFrame]] = None, data_func: Optional[Callable[[], Union[dict, list, tuple, ndarray, spmatrix, DataFrame]]] = None, test_data: Optional[Union[dict, list, tuple, ndarray, spmatrix, DataFrame]] = None, preprocess_data: bool = True, **cloudpickle_kwargs) → RegressionExperiment

Load an experiment saved with save_experiment from path or file.

The data (and test data) is NOT saved with the experiment and will need to be specified again.

path_or_file: str or BinaryIO (file pointer): The path/file pointer to load the experiment from. The pickle file must be created through save_experiment.
data: dataframe-like: Data set with shape (n_samples, n_features), where n_samples is the number of samples and n_features is the number of features. If data is not a pandas dataframe, it’s converted to one using default column names.
data_func: Callable[[], DATAFRAME_LIKE] = None: The function that generate data (the dataframe-like input). This is useful when the dataset is large, and you need parallel operations such as compare_models. It can avoid broadcasting large dataset from driver to workers. Notice one and only one of data and data_func must be set.
test_data: dataframe-like or None, default = None: If not None, test_data is used as a hold-out set and train_size parameter is ignored. The columns of data and test_data must match.
preprocess_data: bool, default = True: If True, the data will be preprocessed again (through running setup internally). If False, the data will not be preprocessed. This means you can save the value of the data attribute of an experiment separately, and then load it separately and pass it here with preprocess_data set to False. This is an advanced feature. We recommend leaving it set to True and passing the same data as passed to the initial setup call.
**cloudpickle_kwargs:: Kwargs to pass to the cloudpickle.load call.

Returns: loaded experiment

pycaret.regression.get_leaderboard(finalize_models: bool = False, model_only: bool = False, fit_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, verbose: bool = True) → DataFrame

This function returns the leaderboard of all models trained in the current setup.

Example

>>> from pycaret.regression import get_leaderboard
>>> leaderboard = get_leaderboard()

finalize_models: bool, default = False: If True, will finalize all models in the ‘Model’ column.
model_only: bool, default = False: When set to False, only model object is returned, instead of the entire pipeline.
fit_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the fit method of the model. Ignored if finalize_models is False.
groups: str or array-like, with shape (n_samples,), default = None: Optional group labels when GroupKFold is used for the cross validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in training dataset. When string is passed, it is interpreted as the column name in the dataset containing group labels. Ignored if finalize_models is False.
verbose: bool, default = True: Progress bar is not printed when verbose is set to False.

Returns: pandas.DataFrame

pycaret.regression.set_current_experiment(experiment: RegressionExperiment)

Set the current experiment to be used with the functional API.

experiment: RegressionExperiment: Experiment object to use.

Returns: None

pycaret.regression.get_current_experiment() → RegressionExperiment

Obtain the current experiment object.

Returns: Current RegressionExperiment

pycaret.regression.dashboard(estimator, display_format: str = 'dash', dashboard_kwargs: Optional[Dict[str, Any]] = None, run_kwargs: Optional[Dict[str, Any]] = None, **kwargs)

This function generates the interactive dashboard for a trained model. The dashboard is implemented using ExplainerDashboard (explainerdashboard.readthedocs.io)

Example

>>> from pycaret.datasets import get_data
>>> juice = get_data('juice')
>>> from pycaret.classification import *
>>> exp_name = setup(data = juice,  target = 'Purchase')
>>> lr = create_model('lr')
>>> dashboard(lr)

estimator: scikit-learn compatible object

Trained model object

display_format: str, default = ‘dash’

Render mode for the dashboard. The default is set to dash which will render a dashboard in browser. There are four possible options:

‘dash’ - displays the dashboard in browser
‘inline’ - displays the dashboard in the jupyter notebook cell.
‘jupyterlab’ - displays the dashboard in jupyterlab pane.
‘external’ - displays the dashboard in a separate tab. (use in Colab)

dashboard_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the ExplainerDashboard class.

run_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the run method of ExplainerDashboard.

**kwargs:

Additional keyword arguments to pass to the ClassifierExplainer or RegressionExplainer class.

Returns: ExplainerDashboard

pycaret.regression.convert_model(estimator, language: str = 'python') → str

This function transpiles trained machine learning models into native inference script in different programming languages (Python, C, Java, Go, JavaScript, Visual Basic, C#, PowerShell, R, PHP, Dart, Haskell, Ruby, F#). This functionality is very useful if you want to deploy models into environments where you can’t install your normal Python stack to support model inference.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> lr_java = convert_model(lr, 'java')

estimator: scikit-learn compatible object

Trained model object

language: str, default = ‘python’

Language in which inference script to be generated. Following options are available:

‘python’
‘java’
‘javascript’
‘c’
‘c#’
‘f#’
‘go’
‘haskell’
‘php’
‘powershell’
‘r’
‘ruby’
‘vb’
‘dart’

Returns: str

pycaret.regression.eda(display_format: str = 'bokeh', **kwargs)

This function generates AutoEDA using AutoVIZ library. You must install Autoviz separately pip install autoviz to use this function.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> eda(display_format = 'bokeh')

display_format: str, default = ‘bokeh’: When set to ‘bokeh’ the plots are interactive. Other option is svg for static plots that are generated using matplotlib and seaborn.
**kwargs:: Additional keyword arguments to pass to the AutoVIZ class.

Returns: None

pycaret.regression.check_fairness(estimator, sensitive_features: list, plot_kwargs: dict = {})

There are many approaches to conceptualizing fairness. This function follows the approach known as group fairness, which asks: Which groups of individuals are at risk for experiencing harms. This function provides fairness-related metrics between different groups (also called subpopulation).

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> lr_fairness = check_fairness(lr, sensitive_features = ['chas'])

estimator: scikit-learn compatible object: Trained model object
sensitive_features: list: Sensitive features are relevant groups (also called subpopulations). You must pass a list of column names that are present in the dataset as string.
plot_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the matplotlib plot.

Returns: pandas.DataFrame

pycaret.regression.create_api(estimator, api_name: str, host: str = '127.0.0.1', port: int = 8000) → None

This function takes an input estimator and creates a POST API for inference. It only creates the API and doesn’t run it automatically. To run the API, you must run the Python file using !python.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> create_api(lr, 'lr_api')
>>> !python lr_api.py #to run the API

estimator: scikit-learn compatible object: Trained model object
api_name: str: Name of the api as a string.
host: str, default = ‘127.0.0.1’: API host address.
port: int, default = 8000: port for API.

Returns: None

pycaret.regression.create_docker(api_name: str, base_image: str = 'python:3.8-slim', expose_port: int = 8000) → None

This function creates a Dockerfile and requirements.txt for productionalizing API end-point.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> create_api(lr, 'lr_api')
>>> create_docker('lr_api')

api_name: str: Name of API. Must be saved as a .py file in the same folder.
base_image: str, default = “python:3.8-slim”: Name of the base image for Dockerfile.
expose_port: int, default = 8000: port for expose for API in the Dockerfile.

Returns: None

pycaret.regression.create_app(estimator, app_kwargs: Optional[dict] = None) → None

This function creates a basic gradio app for inference. It will later be expanded for other app types such as Streamlit.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> lr = create_model('lr')
>>> create_app(lr)

estimator: scikit-learn compatible object: Trained model object
app_kwargs: dict, default = {} (empty dict): arguments to be passed to app class.

Returns: None

pycaret.regression.get_allowed_engines(estimator: str) → Optional[str]

Get all the allowed engines for the specified model :param estimator: Identifier for the model for which the engines should be retrieved,

e.g. “auto_arima”

Returns: The allowed engines for the model. If the model only supports the default engine, then it return None.
Return type: Optional[str]

pycaret.regression.get_engine(estimator: str) → Optional[str]

Gets the model engine currently set in the experiment for the specified model. :param estimator: Identifier for the model for which the engine should be retrieved,

e.g. “auto_arima”

Returns: The engine for the model. If the model only supports the default sktime engine, then it return None.
Return type: Optional[str]

pycaret.regression.check_drift(reference_data: Optional[DataFrame] = None, current_data: Optional[DataFrame] = None, target: Optional[str] = None, numeric_features: Optional[List[str]] = None, categorical_features: Optional[List[str]] = None, date_features: Optional[List[str]] = None, filename: Optional[str] = None) → str

This function generates a drift report file using the evidently library.

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.classification import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> check_drift()

reference_data: Optional[pd.DataFrame] = None: Reference data. If not specified, will use training data. Must be specified if setup() has not been run.
current_data: Optional[pd.DataFrame] = None: Current data. If not specified, will use test data. Must be specified if setup() has not been run.
target: Optional[str] = None: Name of the target column. If not specified, will use the column specified in setup(). Must be specified if setup() has not been run.
numeric_features: Optional[List[str]] = None: Names of numeric columns. If not specified, will use the columns specified/inferred in setup(), or all non-categorical and non-date columns otherwise.
categorical_features: Optional[List[str]] = None: Names of categorical columns. If not specified, will use the columns specified/inferred in setup(). Must be specified if setup() has not been run.
date_features: Optional[List[str]] = None: Names of date columns. If not specified, will use the columns specified/inferred in setup(). Must be specified if setup() has not been run.
filename: Optional[str] = None: Path to save the generated HTML file to. If not specified, will default to ‘[EXPERIMENT_NAME]_[TIMESTAMP]_Drift_Report.html’.

Returns: Path the generated HTML file was saved to.