score
Calculate the NDCG@top metric for the objects in the given dataset.
Method call format
score(X,
y=None,
group_id=None,
top=None,
type=None,
denominator=None,
group_weight=None,
thread_count=-1)
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-dimensional array of numeric values.
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.Series
Default value
None
Supported processing units
CPU and GPU
group_id
Description
A ranking group.
Note
Do not use this parameter if the input training dataset (specified in the X parameter) type is catboost.Pool.
Possible types
- numpy.ndarray
- pandas.DataFrame
- pandas.Series
Default value
None
top
Description
NDCG, Number of top-ranked objects to calculate NDCG
Possible types
- unsigned integer, up to
pow(2, 32) / 2 - 1
Default value
None
type
Description
Metric type: Base or Exp.
Possible types
- str
Default value
None
denominator
Description
Denominator type.
Possible types
- str
Default value
None
group_weight
Description
The weights of all objects within the defined groups from the input data in the form of one-dimensional array-like data.
Used for calculating the final values of trees. By default, it is set to one for all objects in all groups.
Only a weight or group_weight parameter can be used at the time.
Possible types
- list
- numpy.ndarray
- pandas.DataFrame
- pandas.Series
Default value
None
thread_count
Description
The number of threads to use.
Optimizes the speed of execution. This parameter doesn't affect results.
Possible types
- int
Default value
-1 (the number of threads is equal to the number of processor cores)
Type of return value
float