analyze

CrossValidationReport.data.analyze(*, with_y=True, subsample=None, subsample_strategy='head', seed=None)

Plot dataset statistics.

Parameters:

with_ybool, default=True

Whether to include the target variable in the analysis. If True, the target variable is concatenated horizontally to the features.

subsampleint, default=None

The number of points to subsample the dataframe hold by the display, using the strategy set by subsample_strategy. It must be a strictly positive integer. If None, no subsampling is applied.

subsample_strategy{“head”, “random”}, default=”head”

The strategy used to subsample the dataframe hold by the display. It only has an effect when subsample is not None.

• If 'head': subsample by taking the subsample first points of the dataframe, similar to Pandas: df.head(n).

• If "random": randomly subsample the dataframe by using a uniform distribution. The random seed is controlled by seed.

seedint, default=None

The random seed to use when randomly subsampling. It only has an effect when subsample is not None and subsample_strategy='random'.

Returns:

TableReportDisplay

A display object containing the dataset statistics and plots.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import evaluate
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression()
>>> report = evaluate(classifier, X, y, splitter=2)
>>> report.data.analyze().frame()