pycaret.classification.check_drift#

pycaret.classification.check_drift(reference_data: DataFrame | None = None, current_data: DataFrame | None = None, target: str | None = None, numeric_features: List[str] | None = None, categorical_features: List[str] | None = None, date_features: List[str] | None = None, filename: str | None = None) → str[source]#

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

Example

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

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