plot_pca

method plot_pca(title=None, figsize=(10, 6), filename=None, display=True) [source]

Plot the explained variance ratio vs the 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 blue star marks the number of components selected by the user. Only available if PCA was applied on the data.

Parameters:

title: str or None, optional (default=None)
Plot's title. If None, the title is left empty.

figsize: tuple, optional (default=(10, 6))
Figure's size, format as (x, y).

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

Example

from atom import ATOMClassifier

atom = ATOMClassifier(X, y)
atom.feature_selection(strategy="PCA", n_features=11)
atom.plot_pca()