Release: PyCaret 2.3.0 | Release Date: February 21, 2021

Modules Impacted: pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly pycaret.arules pycaret.nlp

Summary of Changes

• Added new interactive residual plots in the pycaret.regression module. You can now generate interactive residual plots by using residuals_interactive in the plot_model function.
• Added plot rendering support for streamlit applications. A new parameter display_format is added in the plot_model function. To render plot in streamlit app, set this to streamlit.
• Revamped Boruta feature selection algorithm. (give it a try)
• tune_model in pycaret.classification and pycaret.regression is now compatible with custom models.
• Added low_memory and max_len support to association rules module (#1008)
• Increased robustness of DataFrame checks (#1005)
• Improved loading of models from AWS (#1005)
• Catboost and XGBoost are now optional dependencies. They are not automatically installed with default slim installation. To install optional dependencies use pip install pycaret[full].
• Added raw_score argument in the predict_model function for pycaret.classification module. When set to True, scores for each class will be returned separately.
• PyCaret now returns base scikit-learn objects, whenever possible
• When handle_unknown_categorical is set to False in the setup function, an exception will be raised during prediction if the data contains unknown levels in categorical features.
• predict_model for multiclass classification now returns labels as an integer.
• Fixed an edge case where an IndexError would be raised in pycaret. clustering and pycaret. anomaly
• Fixed text formatting for certain plots in pycaret.classification and pycaret.regression.
• If a logs.log file cannot be created when setup is initialized, no exception will be raised now (support for more configurable logging to come in the future)
• User added metrics will not raise exceptions now and instead return 0.0
• Compatibility with tune-sklearn>=0.2.0
• Fixed an edge case for dropping NaNs in the target column.
• Fixed stacked models not being tuned correctly.
• Fixed an exception with KFold when fold_shuffle=False.