PermutationImportanceDisplay

class skore.PermutationImportanceDisplay(*, importances, report_type)

Display to inspect feature importance via feature permutation.

Parameters:

importancespd.DataFrame

The importances computed after permuting the input features. The columns are:

• estimator

• data_source

• metric

• split

• feature

• label or output (classification vs. regression)

• repetition

• value

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

Report type from which the display is created.

Attributes:

facet_seaborn FacetGrid

FacetGrid containing the permutation importance.

figure_matplotlib Figure

Figure containing the permutation importance.

ax_matplotlib Axes

Axes with permutation importance.

frame(*, metric=None, aggregate=('mean', 'std'), level='splits', select_k=None, sorting_order=None)

Get the feature importance in a dataframe format.

Parameters:

metricstr or list of str, default=None

Filter the importances by metric. If None, all importances associated with each metric are returned.

aggregate{“mean”, “std”}, (“mean”, std) or None, default=(“mean”, “std”)

How to aggregate the importances. Applied on repetitions or on repetitions then splits, for (comparisons of) cross validation reports and depending on the value of level.

level{“splits”, “repetitions”}, default=”splits”

Over which dimensions to aggregate when aggregate is not None. "repetitions" aggregates only over repetitions (keeps split for cross-validation). "splits" aggregates over repetitions then over splits. Only relevant when aggregate is not None and the report is a CrossValidationReport or a ComparisonReport containing such type of report.

select_kint, default=None

Select features by importance:

• Positive values: the select_k features with largest importance

• Negative values: the -select_k features with smallest importance

Selection is performed independently within each group (estimator, and per label/output if applicable). For cross-validation, features are ranked by mean importance across splits. When aggregate is None, ranking uses mean importance per feature over repetitions (and splits); all repetition/split rows are kept for the selected features.

sorting_order{“descending”, “ascending”, None}, default=None

Sort features by importance: “descending” (most important first), “ascending” (least important first), or None to preserve original order. Can be used independently of select_k. When aggregate is None, ordering uses mean importance per feature over repetitions (and splits).

Returns:

pd.DataFrame

The feature importances. The columns depend on the report type and parameters, and include:

• data_source: Data source used to compute the importances ("train" or "test").

• metric: Metric used to compute the importances.

• feature: Feature name.

• value_mean and value_std: Aggregated importance values (only when aggregate is not None).

• value: Raw importance value per repetition (only when aggregate is None).

• estimator: Name of the estimator (for comparison reports).

• split: Cross-validation split index (for cross-validation reports, only when aggregate is None).

• label: Class label (for multiclass classification).

• output: Output index (for multi-output regression).

help()

Display display help using rich or HTML.

plot(*, metric=None, subplot_by='auto', select_k=None, sorting_order=None)

Plot the permutation importance.

Parameters:

metricstr or None, default=None

Metric to plot. Required when multiple metrics are computed.

subplot_bystr or None, default=”auto”

Column to use for subplotting. The possible values are:

• if "auto", depending of the information available, a meaningful decision is made to create subplots.

• if a string, the corresponding column of the dataframe is used to create several subplots. Those plots will be organized in a grid of a single row and several columns.

• if None, all information is plotted on a single plot. An error is raised if there is too much information to plot on a single plot.

select_kint, default=None

If set, only the top (positive) or bottom (negative) k features by importance are shown. See frame() for details.

sorting_order{“descending”, “ascending”, None}, default=None

Sort features by importance before plotting. See frame() for details.

set_style(*, policy='update', boxplot_kwargs=None, stripplot_kwargs=None)

Set the style parameters for the display.

Parameters:

policy{“override”, “update”}, default=”update”

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.

boxplot_kwargsdict, default=None

Keyword arguments to be passed to seaborn.boxplot() for rendering the importances with a EstimatorReport.

stripplot_kwargsdict, default=None

Keyword arguments to be passed to seaborn.stripplot() for rendering the importances with a EstimatorReport.

Returns:

None

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.