Method grid_search

catboost/python-package/catboost/core.py:4415–4520

Exhaustive search over specified parameter values for a model. After calling this method model is fitted and can be used, if not specified otherwise (refit=False). Parameters ---------- param_grid: dict or list of dictionaries Dictionary with par

(self, param_grid, X, y=None, cv=3, partition_random_seed=0,
                    calc_cv_statistics=True, search_by_train_test_split=True,
                    refit=True, shuffle=True, stratified=None, train_size=0.8, verbose=True, plot=False, plot_file=None,
                    log_cout=None, log_cerr=None)

        return cv_result

    def grid_search(self, param_grid, X, y=None, cv=3, partition_random_seed=0,
                    calc_cv_statistics=True, search_by_train_test_split=True,
                    refit=True, shuffle=True, stratified=None, train_size=0.8, verbose=True, plot=False, plot_file=None,
                    log_cout=None, log_cerr=None):
        """
        Exhaustive search over specified parameter values for a model.
        After calling this method model is fitted and can be used, if not specified otherwise (refit=False).

        Parameters
        ----------
        param_grid: dict or list of dictionaries
            Dictionary with parameters names (string) as keys and lists of parameter settings
            to try as values, or a list of such dictionaries, in which case the grids spanned by each
            dictionary in the list are explored.
            This enables searching over any sequence of parameter settings.

        X: numpy.ndarray or pandas.DataFrame or polars.DataFrame or catboost.Pool
            Data to compute statistics on

        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.

        cv: int, cross-validation generator or an iterable, optional (default=None)
            Determines the cross-validation splitting strategy. Possible inputs for cv are:
            - None, to use the default 3-fold cross validation,
            - integer, to specify the number of folds in a (Stratified)KFold
            - one of the scikit-learn splitter classes
                (https://scikit-learn.org/stable/modules/classes.html#splitter-classes)
            - An iterable yielding (train, test) splits as arrays of indices.

        partition_random_seed: int, optional (default=0)
            Use this as the seed value for random permutation of the data.
            Permutation is performed before splitting the data for cross validation.
            Each seed generates unique data splits.
            Used only when cv is None or int.

        search_by_train_test_split: bool, optional (default=True)
            If True, source dataset is splitted into train and test parts, models are trained
            on the train part and parameters are compared by loss function score on the test part.
            After that, if calc_cv_statistics=true, statistics on metrics are calculated
            using cross-validation using best parameters and the model is fitted with these parameters.

            If False, every iteration of grid search evaluates results on cross-validation.
            It is recommended to set parameter to True for large datasets, and to False for small datasets.

        calc_cv_statistics: bool, optional (default=True)
            The parameter determines whether quality should be estimated.
            using cross-validation with the found best parameters. Used only when search_by_train_test_split=True.

        refit: bool (default=True)
            Refit an estimator using the best found parameters on the whole dataset.

        shuffle: bool, optional (default=True)
            Shuffle the dataset objects before parameters searching.