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plot_feature_importance

method plot_feature_importance(models=None, show=None, title=None, legend="lower right", figsize=None, filename=None, display=True)[source]
Plot a model's feature importance.

The sum of importances for all features (per model) is 1. Only available for models whose estimator has a scores_, feature_importances_ or coef attribute.

Parametersmodels: int, str, Model, slice, sequence or None, default=None
Models to plot. If None, all models are selected.

show: int or None, default=None
Number of features (ordered by importance) to show. If None, it shows all features.

title: str, dict or None, default=None
Title for the plot.

legend: str, dict or None, default="lower right"
Legend for the plot. See the user guide for an extended description of the choices.

  • If None: No legend is shown.
  • If str: Location where to show the legend.
  • If dict: Legend configuration.

figsize: tuple or None, default=None
Figure's size in pixels, format as (x, y). If None, it adapts the size to the number of features shown.

filename: str or None, default=None
Save the plot using this name. Use "auto" for automatic naming. The type of the file depends on the provided name (.html, .png, .pdf, etc...). If filename has no file type, the plot is saved as html. If None, the plot is not saved.

display: bool or None, default=True
Whether to render the plot. If None, it returns the figure.

Returnsgo.Figure or None
Plot object. Only returned if display=None.


See Also

plot_parshap

Plot the partial correlation of shap values.

plot_partial_dependence

Plot the partial dependence of features.

plot_permutation_importance

Plot the feature permutation importance of models.


Example

>>> from atom import ATOMClassifier
>>> from sklearn.datasets import load_breast_cancer

>>> X, y = load_breast_cancer(return_X_y=True, as_frame=True)

>>> atom = ATOMClassifier(X, y)
>>> atom.run(["LR", "RF"])
>>> atom.plot_feature_importance(show=10)