Skip to content

plot_pca

method plot_pca(title=None, legend=None, figsize=(900, 600), filename=None, display=True)[source]
Plot the explained variance ratio vs number of components.

If the underlying estimator is PCA (for dense datasets), all possible components are plotted. If the underlying estimator is TruncatedSVD (for sparse datasets), it only shows the selected components. The star marks the number of components selected by the user. This plot is available only when feature selection was applied with strategy="pca".

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

legend: str, dict or None, default=None
Does nothing. Implemented for continuity of the API.

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

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_components

Plot the explained variance ratio per component.

plot_rfecv

Plot the rfecv results.


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.feature_selection("pca", n_features=5)
>>> atom.plot_pca()