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

• feature

• label or output (classification vs. regression)

• repetition

• value

report_type{“estimator”}

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'))

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”)

Aggregate the importances by the given metric.

Returns:

pd.DataFrame

Dataframe containing the importances.

help()

Display available attributes and methods using rich.

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

Plot the permutation importance.

Parameters:

subplot_bystr, tuple of str or None, default=”auto”

Column(s) 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 a organized in a grid of a single row and several columns.

• if a tuple of strings, the corresponding columns of the dataframe are used to create several subplots. Those plots will be a organized in a grid of several rows and columns. The first element of the tuple is the row and the second element is the column.

• 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.

metricstr or list of str, default=None

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

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:

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.