Regression

pycaret.regression.setup(data: Union[DataFrame, Callable[[], DataFrame]], target: str, train_size: float = 0.7, test_data: Optional[DataFrame] = None, preprocess: bool = True, imputation_type: str = 'simple', iterative_imputation_iters: int = 5, categorical_features: Optional[List[str]] = None, categorical_imputation: str = 'constant', categorical_iterative_imputer: Union[str, Any] = 'lightgbm', ordinal_features: Optional[Dict[str, list]] = None, high_cardinality_features: Optional[List[str]] = None, high_cardinality_method: str = 'frequency', numeric_features: Optional[List[str]] = None, numeric_imputation: str = 'mean', numeric_iterative_imputer: Union[str, Any] = 'lightgbm', date_features: Optional[List[str]] = None, ignore_features: Optional[List[str]] = None, normalize: bool = False, normalize_method: str = 'zscore', transformation: bool = False, transformation_method: str = 'yeo-johnson', handle_unknown_categorical: bool = True, unknown_categorical_method: str = 'least_frequent', pca: bool = False, pca_method: str = 'linear', pca_components: Optional[float] = None, ignore_low_variance: bool = False, combine_rare_levels: bool = False, rare_level_threshold: float = 0.1, bin_numeric_features: Optional[List[str]] = None, remove_outliers: bool = False, outliers_threshold: float = 0.05, remove_multicollinearity: bool = False, multicollinearity_threshold: float = 0.9, remove_perfect_collinearity: bool = True, create_clusters: bool = False, cluster_iter: int = 20, polynomial_features: bool = False, polynomial_degree: int = 2, trigonometry_features: bool = False, polynomial_threshold: float = 0.1, group_features: Optional[List[str]] = None, group_names: Optional[List[str]] = None, feature_selection: bool = False, feature_selection_threshold: float = 0.8, feature_selection_method: str = 'classic', feature_interaction: bool = False, feature_ratio: bool = False, interaction_threshold: float = 0.01, transform_target: bool = False, transform_target_method: str = 'box-cox', 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, custom_pipeline: Optional[Union[Any, Tuple[str, Any], List[Any], List[Tuple[str, Any]]]] = None, html: bool = True, session_id: Optional[int] = None, 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, silent: bool = False, verbose: bool = 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
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')

dataUnion[pd.DataFrame, Callable[[], pd.DataFrame]]

Shape (n_samples, n_features), where n_samples is the number of samples and n_features is the number of features. If data is a function, then it should generate the pandas dataframe. If you want to use distributed PyCaret, it is recommended to provide a function to avoid broadcasting large datasets from the driver to workers.

target: str

Name of the target column to be passed in as a string. The target variable can be either binary or multiclass.

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: pandas.DataFrame, default = None

If not None, test_data is used as a hold-out set and train_size parameter is ignored. test_data must be labelled and the shape of data and test_data must match.

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.

imputation_type: str, default = ‘simple’

The type of imputation to use. Can be either ‘simple’ or ‘iterative’.

iterative_imputation_iters: int, default = 5

Number of iterations. Ignored when imputation_type is not ‘iterative’.

categorical_features: list of str, default = None

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

categorical_imputation: str, default = ‘constant’

Missing values in categorical features are imputed with a constant ‘not_available’ value. The other available option is ‘mode’.

categorical_iterative_imputer: str, default = ‘lightgbm’

Estimator for iterative imputation of missing values in categorical features. Ignored when imputation_type is not ‘iterative’.

ordinal_features: dict, default = None

Encode categorical features as ordinal. 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’] }.

high_cardinality_features: list of str, default = None

When categorical features contains many levels, it can be compressed into fewer levels using this parameter. It takes a list of strings with column names that are categorical.

high_cardinality_method: str, default = ‘frequency’

Categorical features with high cardinality are replaced with the frequency of values in each level occurring in the training dataset. Other available method is ‘clustering’ which trains the K-Means clustering algorithm on the statistical attribute of the training data and replaces the original value of feature with the cluster label. The number of clusters is determined by optimizing Calinski-Harabasz and Silhouette criterion.

numeric_features: list of str, default = None

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

numeric_imputation: str, default = ‘mean’

Missing values in numeric features are imputed with ‘mean’ value of the feature in the training dataset. The other available option is ‘median’ or ‘zero’.

numeric_iterative_imputer: str, default = ‘lightgbm’

Estimator for iterative imputation of missing values in numeric features. Ignored when imputation_type is set to ‘simple’.

date_features: list of str, default = None

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

ignore_features: list of str, default = None

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

normalize: bool, default = False

When set to True, it transforms the numeric 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.

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.

handle_unknown_categorical: bool, default = True

When set to True, unknown categorical levels in unseen data are replaced by the most or least frequent level as learned in the training dataset.

unknown_categorical_method: str, default = ‘least_frequent’

Method used to replace unknown categorical levels in unseen data. Method can be set to ‘least_frequent’ or ‘most_frequent’.

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’

The ‘linear’ method performs uses Singular Value Decomposition. Other options are:

kernel: dimensionality reduction through the use of RBF kernel.
incremental: replacement for ‘linear’ pca when the dataset is too large.

pca_components: int or float, default = None

Number of components to keep. if pca_components is a float, it is treated as a target percentage for information retention. When pca_components is an integer it is treated as the number of features to be kept. pca_components must be less than the original number of features. Ignored when pca is not True.

ignore_low_variance: bool, default = False

When set to True, all categorical features with insignificant variances are removed from the data. The variance is calculated using the ratio of unique values to the number of samples, and the ratio of the most common value to the frequency of the second most common value.

combine_rare_levels: bool, default = False

When set to True, frequency percentile for levels in categorical features below a certain threshold is combined into a single level.

rare_level_threshold: float, default = 0.1

Percentile distribution below which rare categories are combined. Ignored when combine_rare_levels 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 the Singular Value Decomposition.

outliers_threshold: float, default = 0.05

The percentage outliers to be removed from the training dataset. Ignored when remove_outliers is not True.

remove_multicollinearity: bool, default = False

When set to True, features with the inter-correlations higher than the defined threshold are removed. When two features are highly correlated with each other, the feature that is less correlated with the target variable is removed. Only considers numeric features.

multicollinearity_threshold: float, default = 0.9

Threshold for correlated features. Ignored when remove_multicollinearity is not True.

remove_perfect_collinearity: bool, default = True

When set to True, perfect collinearity (features with correlation = 1) is removed from the dataset, when two features are 100% correlated, one of it is randomly removed from the dataset.

create_clusters: bool, default = False

When set to True, an additional feature is created in training dataset where each instance is assigned to a cluster. The number of clusters is determined by optimizing Calinski-Harabasz and Silhouette criterion.

cluster_iter: int, default = 20

Number of iterations for creating cluster. Each iteration represents cluster size. Ignored when create_clusters is not True.

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.

trigonometry_features: bool, default = False

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

polynomial_threshold: float, default = 0.1

When polynomial_features or trigonometry_features is True, new features are derived from the existing numeric features. This may sometimes result in too large feature space. polynomial_threshold parameter can be used to deal with this problem. It does so by using combination of Random Forest, AdaBoost and Linear correlation. All derived features that falls within the percentile distribution are kept and rest of the features are removed.

group_features: list or list of list, default = None

When the dataset contains features with related characteristics, group_features parameter can be used for feature extraction. It takes a list of strings with column names that are related.

group_names: list, default = None

Group names to be used in naming new features. When the length of group_names does not match with the length of group_features, new features are named sequentially group_1, group_2, etc. It is ignored when group_features is None.

feature_selection: bool, default = False

When set to True, a subset of features are selected using a combination of various permutation importance techniques including Random Forest, Adaboost and Linear correlation with target variable. The size of the subset is dependent on the feature_selection_threshold parameter.

feature_selection_threshold: float, default = 0.8

Threshold value used for feature selection. When polynomial_features or feature_interaction is True, it is recommended to keep the threshold low to avoid large feature spaces. Setting a very low value may be efficient but could result in under-fitting.

feature_selection_method: str, default = ‘classic’

Algorithm for feature selection. ‘classic’ method uses permutation feature importance techniques. Other possible value is ‘boruta’ which uses boruta algorithm for feature selection.

feature_interaction: bool, default = False

When set to True, new features are created by interacting (a * b) all the numeric variables in the dataset. This feature is not scalable and may not work as expected on datasets with large feature space.

feature_ratio: bool, default = False

When set to True, new features are created by calculating the ratios (a / b) between all numeric variables in the dataset. This feature is not scalable and may not work as expected on datasets with large feature space.

interaction_threshold: bool, default = 0.01

Similar to polynomial_threshold, It is used to compress a sparse matrix of newly created features through interaction. Features whose importance based on the combination of Random Forest, AdaBoost and Linear correlation falls within the percentile of the defined threshold are kept in the dataset. Remaining features are dropped before further processing.

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 = ‘box-cox’

‘Box-cox’ and ‘yeo-johnson’ methods are supported. Box-Cox requires input data to be strictly positive, while Yeo-Johnson supports both positive or negative data. When transform_target_method is ‘box-cox’ and target variable contains negative values, method is internally forced to ‘yeo-johnson’ to avoid exceptions.

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

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 Regressor, 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

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

When passed, will append the custom transformers in the preprocessing pipeline and are applied on each CV fold separately and on the final fit. All the custom transformations are applied after ‘train_test_split’ and before pycaret’s internal transformations.

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’) corresponding to a logger to determine which experiment loggers to use. Setting to True will use just MLFlow. If wandb (Weights & Biases) is installed, will also log there.

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.

silent: bool, default = False

Controls the confirmation input of data types when setup is executed. When executing in completely automated mode or on a remote kernel, this must be True.

verbose: bool, default = True

When set to False, Information grid is not printed.

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.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, verbose: bool = True, display: Optional[Display] = None, 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.
display: pycaret.internal.Display.Display, default = None: Custom display object
parallel: pycaret.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
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.
verbose: bool, default = True: Score grid is not printed when verbose is set to False.

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.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, 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.

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.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 = False, 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 = False

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.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.
>>> 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.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 = True: 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) → 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):

‘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.

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: None

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: bool = False, **kwargs)

This function analyzes the predictions generated from a trained model. Most plots in this function are implemented based on the SHAP (SHapley Additive exPlanations). For more info on this, please see https://shap.readthedocs.io/en/latest/

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 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: bool, default = False

When set to True, Plot is saved as a ‘png’ file in current working directory.

**kwargs:

Additional keyword arguments to pass to the plot.

Returns: None

pycaret.regression.predict_model(estimator, data: Optional[DataFrame] = None, drift_report: bool = False, round: int = 4, verbose: bool = True, drift_kwargs: Optional[dict] = None) → 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.
drift_report: bool, default = False: When set to True, interactive drift report is generated on test set with the evidently library.
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 = True, experiment_custom_tags: Optional[Dict[str, Any]] = None, return_train_score: bool = False) → 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_only: bool, default = True: When set to False, only model object is re-trained and all the transformations in Pipeline are ignored.
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.
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.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, 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, 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.
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 last printed score grid. Use pull function after any training function to store the score grid in pandas.DataFrame.

pop: bool, default = False: If True, will pop (remove) the returned dataframe from the display container.

Returns: 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 for CV.

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 for CV.

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 CV.

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: str)

This function retrieves the global variables created when initializing the setup function. Following variables are accessible:

X: Transformed dataset (X)
y: Transformed dataset (y)
X_train: Transformed train dataset (X)
X_test: Transformed test/holdout dataset (X)
y_train: Transformed train dataset (y)
y_test: Transformed test/holdout dataset (y)
seed: random state set through session_id
prep_pipe: Transformation pipeline
fold_shuffle_param: shuffle parameter used in Kfolds
n_jobs_param: n_jobs parameter used in model training
html_param: html_param configured through setup
create_model_container: results grid storage container
master_model_container: model storage container
display_container: results display container
exp_name_log: Name of experiment
logging_param: log_experiment param
log_plots_param: log_plots param
USI: Unique session ID parameter
fix_imbalance_param: fix_imbalance param
fix_imbalance_method_param: fix_imbalance_method param
data_before_preprocess: data before preprocessing
target_param: name of target variable
gpu_param: use_gpu param configured through setup
fold_generator: CV splitter configured in fold_strategy
fold_param: fold params defined in the setup
fold_groups_param: fold groups defined in the setup
stratify_param: stratify parameter defined in the setup
transform_target_param: transform_target_param in setup
transform_target_method_param: transform_target_method_param in setup

Example

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

Returns: Global variable

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

This function resets the global variables. Following variables are accessible:

X: Transformed dataset (X)
y: Transformed dataset (y)
X_train: Transformed train dataset (X)
X_test: Transformed test/holdout dataset (X)
y_train: Transformed train dataset (y)
y_test: Transformed test/holdout dataset (y)
seed: random state set through session_id
prep_pipe: Transformation pipeline
fold_shuffle_param: shuffle parameter used in Kfolds
n_jobs_param: n_jobs parameter used in model training
html_param: html_param configured through setup
create_model_container: results grid storage container
master_model_container: model storage container
display_container: results display container
exp_name_log: Name of experiment
logging_param: log_experiment param
log_plots_param: log_plots param
USI: Unique session ID parameter
fix_imbalance_param: fix_imbalance param
fix_imbalance_method_param: fix_imbalance_method param
data_before_preprocess: data before preprocessing
target_param: name of target variable
gpu_param: use_gpu param configured through setup
fold_generator: CV splitter configured in fold_strategy
fold_param: fold params defined in the setup
fold_groups_param: fold groups defined in the setup
stratify_param: stratify parameter defined in the setup
transform_target_param: transform_target_param in setup
transform_target_method_param: transform_target_method_param in setup

Example

>>> from pycaret.datasets import get_data
>>> boston = get_data('boston')
>>> from pycaret.regression import *
>>> exp_name = setup(data = boston,  target = 'medv')
>>> set_config('seed', 123)

Returns: None

pycaret.regression.save_config(file_name: str)

This function save all global variables to a pickle file, allowing to later resume without rerunning the setup.

Example

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

Returns: None

pycaret.regression.load_config(file_name: str)

This function loads global variables from a pickle file into Python environment.

Example

>>> from pycaret.regression import load_config
>>> load_config('myvars.pkl')

Returns: Global variables

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.dashboard(estimator, display_format='dash', dashboard_kwargs={}, run_kwargs={}, **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: None

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 = export_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(data=None, target: Optional[str] = None, 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')

data: pandas.DataFrame: DataFrame with (n_samples, n_features).
target: str: Name of the target column to be passed in as a string.
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 = {}: arguments to be passed to app class.

Returns: None

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

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

Returns: None