Method fit

catboost/python-package/catboost/core.py:2758–2898

Fit the CatBoost model. Parameters ---------- X : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series or polars.DataFrame or string. If not catboost.Pool or catboost.FeaturesData it must be 2 dimensional Feature matri

(self, X, y=None, cat_features=None, text_features=None, embedding_features=None, pairs=None, graph=None, sample_weight=None, group_id=None,
            group_weight=None, subgroup_id=None, pairs_weight=None, baseline=None, use_best_model=None,
            eval_set=None, verbose=None, logging_level=None, plot=False, plot_file=None, column_description=None,
            verbose_eval=None, metric_period=None, silent=None, early_stopping_rounds=None,
            save_snapshot=None, snapshot_file=None, snapshot_interval=None, init_model=None, callbacks=None,
            log_cout=None, log_cerr=None)

        return self

    def fit(self, X, y=None, cat_features=None, text_features=None, embedding_features=None, pairs=None, graph=None, sample_weight=None, group_id=None,
            group_weight=None, subgroup_id=None, pairs_weight=None, baseline=None, use_best_model=None,
            eval_set=None, verbose=None, logging_level=None, plot=False, plot_file=None, column_description=None,
            verbose_eval=None, metric_period=None, silent=None, early_stopping_rounds=None,
            save_snapshot=None, snapshot_file=None, snapshot_interval=None, init_model=None, callbacks=None,
            log_cout=None, log_cerr=None):
        """
        Fit the CatBoost model.

        Parameters
        ----------
        X : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series or polars.DataFrame
             or string.
            If not catboost.Pool or catboost.FeaturesData it must be 2 dimensional Feature matrix
             or string - file with dataset.

             Must be non-empty (contain > 0 objects)

        y : list or numpy.ndarray or pandas.DataFrame or pandas.Series or polars.DataFrame or polars.Series, optional (default=None)
            Labels of the training data.
            If not None, can be a single- or two- dimensional array with either:
              - numerical values - for regression (including multiregression), ranking and binary classification problems
              - class labels (boolean, integer or string) - for classification (including multiclassification) problems
            Use only if X is not catboost.Pool and does not point to a file.

        cat_features : list or numpy.ndarray, optional (default=None)
            If not None, giving the list of Categ columns indices.
            Use only if X is not catboost.Pool and not catboost.FeaturesData

        text_features: list or numpy.ndarray, optional (default=None)
            If not none, giving the list of Text columns indices.
            Use only if X is not catboost.Pool and not catboost.FeaturesData

        embedding_features: list or numpy.ndarray, optional (default=None)
            If not none, giving the list of Embedding columns indices.
            Use only if X is not catboost.Pool and not catboost.FeaturesData

        pairs : list or numpy.ndarray or pandas.DataFrame or polars.DataFrame
            The pairs description.
            If list or numpy.ndarrays or pandas.DataFrame or polars.DataFrame, giving 2 dimensional.
            The shape should be Nx2, where N is the pairs' count. The first element of the pair is
            the index of the winner object in the training set. The second element of the pair is
            the index of the loser object in the training set.

        graph : list or numpy.ndarray or pandas.DataFrame or polars.DataFrame
            The graph edges list description.
            If list or numpy.ndarrays or pandas.DataFrame, giving 2 dimensional.

        sample_weight : list or numpy.ndarray or pandas.DataFrame or pandas.Series or polars.Series, optional (default=None)
            Instance weights, 1 dimensional array like.

        group_id : list or numpy.ndarray or polars.Series, optional (default=None)
            group id for each instance.
            If not None, giving 1 dimensional array like data.
            Use only if X is not catboost.Pool.

        group_weight : list or numpy.ndarray or polars.Series, optional (default=None)
            Group weight for each instance.