CrossValidationReport.feature_importance.coefficients#

CrossValidationReport.feature_importance.coefficients()[source]#

Retrieve the coefficients across splits, including the intercept.

Returns:
FeatureImportanceCoefficientsDisplay

The feature importance display containing model coefficients and intercept.

Examples

>>> from sklearn.datasets import make_regression
>>> from sklearn.linear_model import Ridge
>>> from skore import CrossValidationReport
>>> X, y = make_regression(n_features=3, random_state=42)
>>> report = CrossValidationReport(
>>>     estimator=Ridge(), X=X, y=y, splitter=5, n_jobs=4
>>> )
>>> display = report.feature_importance.coefficients()
>>> display.frame()
            Intercept   Feature #0      Feature #1      Feature #2
Split index
0               0.064837        74.100966       27.309656       17.367865
1               0.030257        74.276481       27.571421       17.392395
2               0.000084        74.107126       27.614821       17.277730
3               0.145613        74.207645       27.523667       17.391055
4               0.033695        74.259575       27.599610       17.390481
>>> display.plot() # shows plot