holisticai.explainability.plots.plot_feature_importance#

holisticai.explainability.plots.plot_feature_importance(feature_importance: Importances, alpha=0.8, top_n=20, ax=None)[source]#

Bar plot of ranked feature importance.

Parameters

feature_importance: Importances: The feature importance data.
top_n: (int, optional): The number of top features to display. Defaults to 20.
alpha: (float, optional): Percentage of importance to consider as top features. Defaults to 0.8.
ax: (matplotlib.axes.Axes, optional): The matplotlib axes to plot on. If not provided, a new figure and axes will be created.

Returns

matplotlib.axes.Axes: The matplotlib axes object containing the plot.

Example

>>> feature_importance = Importances(
...     values=np.array([0.1, 0.2, 0.3, 0.4]), feature_names=["A", "B", "C", "D"]
... )
>>> plot_feature_importance(feature_importance)

The plot should look like this: