FeatureImportanceCoefficientsDisplay

class skore.FeatureImportanceCoefficientsDisplay(report_type, coefficients)

Feature importance display.

Each report type produces its own output frame and plot.

Parameters:

report_type{“estimator”, “cross-validation”, “comparison-estimator”, “comparison-cross-validation”}

Report type from which the display is created.

coefficientsDataFrame | list[DataFrame]

The coefficients data to display.

Attributes:

ax_matplotlib Axes

Axes with the different matplotlib axis.

figure_matplotlib Figure

Figure containing the plot.

Examples

>>> from sklearn.datasets import load_diabetes
>>> from sklearn.linear_model import LinearRegression
>>> from skore import train_test_split
>>> from skore import EstimatorReport
>>> X, y = load_diabetes(return_X_y=True)
>>> split_data = train_test_split(
>>>     X=X, y=y, random_state=0, as_dict=True, shuffle=False
>>> )
>>> report = EstimatorReport(LinearRegression(), **split_data)
>>> display = report.feature_importance.coefficients()
>>> display.plot()
>>> display.frame()
            Coefficient
Intercept   151.487952
Feature #0  -11.861904
Feature #1  -238.445509
Feature #2  505.395493
Feature #3  298.977119
...         ...

frame()

Return coefficients as a DataFrame.

Returns:

pd.DataFrame

The structure of the returned frame depends on the underlying report type:

• If an EstimatorReport, a single column “Coefficient”, with the index being the feature names.

• If a CrossValidationReport, the columns are the feature names, and the index is the respective split number.

• If a ComparisonReport, the columns are the models passed in the report, with the index being the feature names.

help()

Display available attributes and methods using rich.

plot(**kwargs)

Plot the coefficients of linear models.

Parameters:

**kwargsdict

Additional keyword arguments to be passed to the plot method.

set_style(*, policy='override', **kwargs)

Set the style parameters for the display.

Parameters:

policyLiteral[“override”, “update”], default=”override”

Policy to use when setting the style parameters. If “override”, existing settings are set to the provided values. If “update”, existing settings are not changed; only settings that were previously unset are changed.

**kwargsdict

Style parameters to set. Each parameter name should correspond to a a style attribute passed to the plot method of the display.

Returns:

selfobject

Returns the instance itself.

Raises:

ValueError

If a style parameter is unknown.

static style_plot(plot_func)

Apply consistent style to skore displays.

This decorator: 1. Applies default style settings 2. Executes plot_func 3. Calls plt.tight_layout() to make sure axis does not overlap 4. Restores the original style settings

Parameters:

plot_funccallable

The plot function to be decorated.

Returns:

callable

The decorated plot function.