pycaret.classification.get_leaderboard#

pycaret.classification.get_leaderboard(finalize_models: bool = False, model_only: bool = False, fit_kwargs: dict | None = None, groups: str | Any | None = None, verbose: bool = True) → DataFrame[source]#

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

Example

>>> from pycaret.classification 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