score

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

Method call format

score(X, y)

Parameters

Parameter Possible types Description Default value
X catboost.Pool

The input training dataset.

Required parameter
  • list
  • numpy.array
  • pandas.DataFrame
  • pandas.Series

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

y
  • list
  • numpy.array
  • pandas.DataFrame
  • pandas.Series

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

Must be in the form of a one-dimensional array. The type of data in the array depends on the machine learning task being solved:
  • Regression and ranking  — Numeric values.
  • Binary classification — 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 grater than the value of the border parameter of the loss function. 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 — Integers or strings that represents the labels of the classes.
None
Parameter Possible types Description Default value
X catboost.Pool

The input training dataset.

Required parameter
  • list
  • numpy.array
  • pandas.DataFrame
  • pandas.Series

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

y
  • list
  • numpy.array
  • pandas.DataFrame
  • pandas.Series

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

Must be in the form of a one-dimensional array. The type of data in the array depends on the machine learning task being solved:
  • Regression and ranking  — Numeric values.
  • Binary classification — 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 grater than the value of the border parameter of the loss function. 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 — Integers or strings that represents the labels of the classes.
None

Type of return value

float