plot_permutation_importance

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method plot_permutation_importance(models=None, show=None, n_repeats=10, title=None, figsize=None, filename=None, display=True) [source]

Plot the feature permutation importance of models. Calculating permutations can be time-consuming, especially if n_repeats is high. For this reason, the permutations are stored under the permutations attribute. If the plot is called again for the same model with the same n_repeats, it will use the stored values, making the method considerably faster. The trainer's feature_importance attribute is updated with the extracted importance ranking.

Parameters:

models: str, sequence or None, optional (default=None) Name of the models to plot. If None, all models in the pipeline are selected. show: int, optional (default=None) Number of features (ordered by importance) to show. None to show all. n_repeats: int, optional (default=10) Number of times to permute each feature. title: str or None, optional (default=None) Plot's title. If None, the title is left empty. figsize: tuple or None, optional (default=None) Figure's size, format as (x, y). If None, it adapts the size to the number of features shown. filename: str or None, optional (default=None) Name of the file. Use "auto" for automatic naming. If None, the figure is not saved. display: bool or None, optional (default=True) Whether to render the plot. If None, it returns the matplotlib figure.

Returns:

fig: matplotlib.figure.Figure Plot object. Only returned if display=None.

Example

from atom import ATOMClassifier

atom = ATOMClassifier(X, y)
atom.run(["LR", "LDA"], metric="average_precision")
atom.lda.plot_permutation_importance(show=10, n_repeats=7)