holisticai.explainability.plots.plot_partial_dependence#

holisticai.explainability.plots.plot_partial_dependence(partial_dependence: PartialDependence, ranked_feature_importance: Importances, subplots=(1, 1), figsize=None, class_idx=0)[source]#

Plots the partial dependence of features on the predicted target.

Parameters

partial_dependence: PartialDependence

The partial dependence object containing the computed partial dependence values.

ranked_feature_importance: RankedFeatureImportance

The ranked feature importance object containing the feature names and their importance scores.

subplots: (tuple, optional)

The shape of the subplots grid. Defaults to (1, 1).

figsize: (tuple, optional)

The size of the figure. Defaults to None.

Returns

fig: The matplotlib figure object containing the plot.

Example

>>> partial_dependence = PartialDependence(values=[...])
>>> ranked_feature_importance = Importances(values=[...], feature_names=[...])
>>> plot_partial_dependence(partial_dependence, ranked_feature_importance)

The plot should look like this:

Plot Partial Dependence