score

  1. Method call format
  2. Parameters
  3. Type of return value

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
  • string

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
  • string

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