score
Calculate the R2 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:
- Regression — One-dimensional array of numeric values.
- Multiregression - Two-dimensional array of numeric values. The first index is for a dimension, the second index is for an object.
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