score

Calculate the Accuracy metric for the objects in the given dataset.

Method call format

score(X, y)

Parameters

X

Description

The description is different for each group of possible types.

Possible types

catboost.Pool

The input training dataset.

Note

If a nontrivial value of the cat_features parameter is specified in the constructor of this class, CatBoost checks the equivalence of categorical features indices specification from the constructor parameters and in this Pool class.

list, numpy.ndarray, pandas.DataFrame, pandas.Series

The input training dataset in the form of a two-dimensional feature matrix.

pandas.SparseDataFrame, scipy.sparse.spmatrix (all subclasses except dia_matrix)

The input training dataset in the form of a two-dimensional sparse feature matrix.

Default value

Required parameter

y

Description

The target variables (in other words, the objects' label values) for the evaluation dataset.

Must be in the form of a one- or two- dimensional array. The type of data in the array depends on the machine learning task being solved:

  • Binary classification
    One-dimensional array containing one of:

    • Booleans, integers or strings that represent the labels of the classes (only two unique values).

    • Numeric values.
      The interpretation of numeric values depends on the selected loss function:

      • Logloss — The value is considered a positive class if it is strictly greater than the value of the target_border training parameter. Otherwise, it is considered a negative class.
      • CrossEntropy — The value is interpreted as the probability that the dataset object belongs to the positive class. Possible values are in the range [0; 1].
  • Multiclassification — One-dimensional array of integers or strings that represent the labels of the classes.

  • Multi label classification
    Two-dimensional array. The first index is for a label/class, the second index is for an object.

    Possible values depend on the selected loss function:

    • MultiLogloss — Only {0, 1} or {False, True} values are allowed that specify whether an object belongs to the class corresponding to the first index.
    • MultiCrossEntropy — Numerical values in the range [0; 1] that are interpreted as the probability that the dataset object belongs to the class corresponding to the first index.

Note

Do not use this parameter if the input training dataset (specified in the X parameter) type is catboost.Pool.

Possible types

  • list
  • numpy.ndarray
  • pandas.DataFrame
  • pandas.Series

Default value

None

Supported processing units

CPU and GPU

Type of return value

float