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

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.

Possible types

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

Default value

None

Supported processing units

CPU and GPU

Type of return value

float