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plot_rfecv

method plot_rfecv(plot_interval=True, title=None, legend="upper right", figsize=(900, 600), filename=None, display=True)[source]
Plot the rfecv results.

Plot the scores obtained by the estimator fitted on every subset of the dataset. Only available when feature selection was applied with strategy="rfecv".

Parametersplot_interval: bool, default=True
Whether to plot the 1-sigma confidence interval.

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

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

  • If None: No legend is shown.
  • If str: Position to display the legend.
  • If dict: Legend configuration.

figsize: tuple, default=(900, 600)
Figure's size in pixels, format as (x, y).

filename: str, Path 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_components

Plot the explained variance ratio per component.

plot_pca

Plot the explained variance ratio vs number of components.


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, random_state=1)
>>> atom.feature_selection("rfecv", solver="Tree")
>>> atom.plot_rfecv()