Class CatBoostClassifier

catboost/python-package/catboost/core.py:4839–5921

Implementation of the scikit-learn API for CatBoost classification. Parameters ---------- iterations : int, [default=500] Max count of trees. range: [1,+inf) learning_rate : float, [default value is selected automatically for binary classification with other par

class CatBoostClassifier(CatBoost):
    """
    Implementation of the scikit-learn API for CatBoost classification.

    Parameters
    ----------
    iterations : int, [default=500]
        Max count of trees.
        range: [1,+inf)
    learning_rate : float, [default value is selected automatically for binary classification with other parameters set to default. In all other cases default is 0.03]
        Step size shrinkage used in update to prevents overfitting.
        range: (0,1]
    depth : int, [default=6]
        Depth of a tree. All trees are the same depth.
        range: [1,16]
    l2_leaf_reg : float, [default=3.0]
        Coefficient at the L2 regularization term of the cost function.
        range: [0,+inf)
    model_size_reg : float, [default=None]
        Model size regularization coefficient.
        range: [0,+inf)
    rsm : float, [default=None]
        Subsample ratio of columns when constructing each tree.
        range: (0,1]
    loss_function : string or object, [default='Logloss']
        The metric to use in training and also selector of the machine learning
        problem to solve. If string, then the name of a supported metric,
        optionally suffixed with parameter description.
        If object, it shall provide methods 'calc_ders_range' or 'calc_ders_multi'.
    border_count : int, [default = 254 for training on CPU or 128 for training on GPU]
        The number of partitions in numeric features binarization. Used in the preliminary calculation.
        range: [1,65535] on CPU, [1,255] on GPU
    feature_border_type : string, [default='GreedyLogSum']
        The binarization mode in numeric features binarization. Used in the preliminary calculation.
        Possible values:
            - 'Median'
            - 'Uniform'
            - 'UniformAndQuantiles'
            - 'GreedyLogSum'
            - 'MaxLogSum'
            - 'MinEntropy'
    per_float_feature_quantization : list of strings, [default=None]
        List of float binarization descriptions.
        Format : described in documentation on catboost.ai
        Example 1: ['0:1024'] means that feature 0 will have 1024 borders.
        Example 2: ['0:border_count=1024', '1:border_count=1024', ...] means that two first features have 1024 borders.
        Example 3: ['0:nan_mode=Forbidden,border_count=32,border_type=GreedyLogSum',
                    '1:nan_mode=Forbidden,border_count=32,border_type=GreedyLogSum'] - defines more quantization properties for first two features.
    input_borders : string or os.PathLike, [default=None]
        input file with borders used in numeric features binarization.
    output_borders : string, [default=None]
        output file for borders that were used in numeric features binarization.
    fold_permutation_block : int, [default=1]
        To accelerate the learning.
        The recommended value is within [1, 256]. On small samples, must be set to 1.
        range: [1,+inf)
    od_pval : float, [default=None]
        Use overfitting detector to stop training when reaching a specified threshold.