inspection.importance.plot_feature_importances

inspection.importance.plot_feature_importances(
    X,
    y,
    feature_names,
    target_names,
    target_index,
    n_top_features=10,
    figsize=(6, 6),
)

Generate and plot feature importances using MDI and permutation importance.

Parameters

Name	Type	Description	Default
X	np.ndarray	Input features array	required
y	np.ndarray	Target array	required
feature_names	list	List of feature names	required
target_names	list	List of target names	required
target_index	int	Index of target variable to analyze	required
n_top_features	int	Number of top features to show	10
figsize	tuple	Size of the figure	(6, 6)

Returns

Name	Type	Description
tuple	tuple	(top_features, importance_df)

Examples

>>> from spotoptim.sensitivity import plot_feature_importances
>>> import pandas as pd
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_samples=100, n_features=5, noise=0.1, random_state=42)
>>> feature_names = [f"feature_{i}" for i in range(X.shape[1])]
>>> target_names = ["target"]
>>> top_features, imp_df = plot_feature_importances(X, y, feature_names, target_names, target_index=0)
>>> print("Top features:", top_features)