Function cv

catboost/python-package/catboost/core.py:7023–7313

Cross-validate the CatBoost model. Parameters ---------- pool : catboost.Pool Data to cross-validate on. params : dict Parameters for CatBoost. CatBoost has many of parameters, all have default values. If None, all params still defaults.

(pool=None, params=None, dtrain=None, iterations=None, num_boost_round=None,
       fold_count=None, nfold=None, inverted=False, partition_random_seed=0, seed=None,
       shuffle=True, logging_level=None, stratified=None, as_pandas=True, metric_period=None,
       verbose=None, verbose_eval=None, plot=False, plot_file=None, early_stopping_rounds=None,
       save_snapshot=None, snapshot_file=None, snapshot_interval=None, metric_update_interval=0.5,
       folds=None, type='Classical', return_models=False, log_cout=None, log_cerr=None)

def cv(pool=None, params=None, dtrain=None, iterations=None, num_boost_round=None,
       fold_count=None, nfold=None, inverted=False, partition_random_seed=0, seed=None,
       shuffle=True, logging_level=None, stratified=None, as_pandas=True, metric_period=None,
       verbose=None, verbose_eval=None, plot=False, plot_file=None, early_stopping_rounds=None,
       save_snapshot=None, snapshot_file=None, snapshot_interval=None, metric_update_interval=0.5,
       folds=None, type='Classical', return_models=False, log_cout=None, log_cerr=None):
    """
    Cross-validate the CatBoost model.

    Parameters
    ----------
    pool : catboost.Pool
        Data to cross-validate on.

    params : dict
        Parameters for CatBoost.
        CatBoost has many of parameters, all have default values.
        If  None, all params still defaults.
        If  dict, overriding some (or all) params.

    dtrain : catboost.Pool or tuple (X, y)
        Synonym for pool parameter. Only one of these parameters should be set.

    iterations : int
        Number of boosting iterations. Can be set in params dict.

    num_boost_round : int
        Synonym for iterations. Only one of these parameters should be set.

    fold_count : int, optional (default=3)
        The number of folds to split the dataset into.

    nfold : int
        Synonym for fold_count.

    type : string, optional (default='Classical')
        Type of cross-validation
        Possible values:
            - 'Classical'
            - 'Inverted'
            - 'TimeSeries'

    inverted : bool, optional (default=False)
        Train on the test fold and evaluate the model on the training folds.

    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.

    seed : int, optional
        Synonym for partition_random_seed. This parameter is deprecated. Use
        partition_random_seed instead.
        If both parameters are initialised partition_random_seed parameter is
        ignored.

    shuffle : bool, optional (default=True)
        Shuffle the dataset objects before splitting into folds.