pycaret.regression.RegressionExperiment#

class pycaret.regression.RegressionExperiment[source]#

__init__() → None[source]#

Methods

`__init__`()
`add_metric`(id, name, score_func[, ...])	Adds a custom metric to be used in the experiment.
`automl`([optimize, use_holdout, turbo, ...])	This function returns the best model out of all trained models in current session based on the `optimize` parameter.
`blend_models`(estimator_list[, fold, round, ...])	This function trains a Voting Regressor for select models passed in the `estimator_list` param.
`check_drift`([reference_data, current_data, ...])	This function generates a drift report file using the evidently library.
`check_fairness`(estimator, sensitive_features)	There are many approaches to conceptualizing fairness.
`compare_models`([include, exclude, fold, ...])	This function trains and evaluates performance of all estimators available in the model library using cross validation.
`convert_model`(estimator[, language])	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#).
`create_api`(estimator, api_name[, host, port])	This function takes an input `estimator` and creates a POST API for inference.
`create_app`(estimator, app_kwargs)	This function creates a basic gradio app for inference.
`create_docker`(api_name[, base_image, ...])	This function creates a `Dockerfile` and `requirements.txt` for productionalizing API end-point.
`create_model`(estimator[, fold, round, ...])	This function trains and evaluates the performance of a given estimator using cross validation.
`dashboard`(estimator[, display_format, ...])	This function generates the interactive dashboard for a trained model.
`deploy_model`(model, model_name, authentication)	This function deploys the transformation pipeline and trained model on cloud.
`ensemble_model`(estimator[, method, fold, ...])	This function ensembles a given estimator.
`evaluate_model`(estimator[, fold, ...])	This function displays a user interface for analyzing performance of a trained model.
`finalize_model`(estimator[, fit_kwargs, ...])	This function trains a given estimator on the entire dataset including the holdout set.
`get_allowed_engines`(estimator)	Get all the allowed engines for the specified estimator
`get_config`([variable])	This function is used to access global environment variables.
`get_engine`(estimator)	Gets the model engine currently set in the experiment for the specified model.
`get_leaderboard`([finalize_models, ...])	generates leaderboard for all models run in current run.
`get_logs`([experiment_name, save])	Returns a table of experiment logs.
`get_metrics`([reset, include_custom, ...])	Returns table of available metrics used in the experiment.
`interpret_model`(estimator[, plot, feature, ...])	This function takes a trained model object and returns an interpretation plot based on the test / hold-out set.
`load_experiment`(path_or_file[, data, ...])	Load an experiment saved with `save_experiment` from path or file.
`load_model`(model_name[, platform, ...])	This function loads a previously saved pipeline.
`models`([type, internal, raise_errors])	Returns table of models available in the model library.
`plot_model`(estimator[, plot, scale, save, ...])	This function analyzes the performance of a trained model on holdout set.
`plot_model_check_display_format_`(display_format)	Checks if the display format is in the allowed list
`predict_model`(estimator[, data, round, verbose])	This function predicts `Label` using a trained model.
`pull`([pop])	Returns the latest displayed table.
`remove_metric`(name_or_id)	Removes a metric from experiment.
`save_experiment`(path_or_file, ...)	Saves the experiment to a pickle file.
`save_model`(model, model_name[, model_only, ...])	This function saves the transformation pipeline and trained model object into the current working directory as a pickle file for later use.
`set_config`([variable, value])	This function is used to reset global environment variables.
`setup`([data, data_func, target, index, ...])	This function initializes the training environment and creates the transformation pipeline.
`stack_models`(estimator_list[, meta_model, ...])	This function trains a meta model over select estimators passed in the `estimator_list` parameter.
`tune_model`(estimator[, fold, round, n_iter, ...])	This function tunes the hyperparameters of a given estimator.

Attributes

`X`	Feature set.
`X_test`	Feature set of the test set.
`X_test_transformed`	Transformed feature set of the test set.
`X_train`	Feature set of the training set.
`X_train_transformed`	Transformed feature set of the training set.
`X_transformed`	Transformed feature set.
`dataset`	Complete dataset without ignored columns.
`dataset_transformed`	Transformed dataset.
`gpu_n_jobs_param`
`is_multiclass`	Method to check if the problem is multiclass.
`test`	Test set.
`test_transformed`	Transformed test set.
`train`	Training set.
`train_transformed`	Transformed training set.
`variable_and_property_keys`
`variables`
`y`	Target column.
`y_test`	Target column of the test set.
`y_test_transformed`	Transformed target column of the test set.
`y_train`	Target column of the training set.
`y_train_transformed`	Transformed target column of the training set.
`y_transformed`	Transformed target column.