plot_lift#

scikitplot.kds.plot_lift(y_true, y_probas, *args, pos_label=None, class_index=1, title='Lift Curves', ax=None, fig=None, figsize=None, title_fontsize='large', text_fontsize='medium', data=None, **kwargs)[source]#

Generates the Decile based cumulative Lift Plot from labels and probabilities.

The lift curve is used to determine the effectiveness of a binary classifier. A detailed explanation can be found at http://www2.cs.uregina.ca/~dbd/cs831/notes/lift_chart/lift_chart.html The implementation here works only for binary classification.

Parameters:

y_truearray-like of shape (n_samples,): Ground truth (correct) target values.
y_probasarray-like of shape (n_samples,) or (n_samples, n_classes): Predicted probabilities for each class or only target class probabilities. If 1D, it is treated as probabilities for the positive class in binary or multiclass classification with the class_index.
class_indexint, optional, default=1: Index of the class of interest for multi-class classification. Ignored for binary classification.
titlestr, default=’Lift Curves’: Title of the plot.
title_fontsizestr or int, optional, default=’large’: Font size for the plot title.
text_fontsizestr or int, optional, default=’medium’: Font size for the text in the plot.
**kwargsdict, optional: Added in version 0.3.9.

Returns:

matplotlib.axes.Axes: The axes with the plotted lift curves.

Other Parameters:

axmpl.axes.Axes, optional: The axes on which to plot. If None, a new one will be created.
figmpl.figure.Figure, optional: The figure in which to place the axes. If None, a new one will be created.
figsizetuple, optional: Size of the figure if a new one is created. Default is None.
nrowsint, optional: Number of rows in the subplot grid. Default is 1.
ncolsint, optional: Number of columns in the subplot grid. Default is 1.
indexint or tuple, optional: The position of the subplot on the grid. It can be: - An integer specifying the position (1-based).
*argstuple, optional: validate_plotting_kwargs properties Positional arguments passed to the function.
**kwargsdict, optional: validate_plotting_kwargs properties Keyword arguments passed to the function.

See also

plot_lift_decile_wise: Generates the Decile-wise Lift Plot from labels and probabilities.

Examples

>>> from sklearn.datasets import load_iris as data_3_classes
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.linear_model import LogisticRegression
>>> import scikitplot as skplt
>>> X, y = data_3_classes(return_X_y=True, as_frame=False)
>>> X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.5, random_state=0)
>>> model = LogisticRegression(max_iter=int(1e5), random_state=0).fit(X_train, y_train)
>>> y_probas = model.predict_proba(X_val)
>>> skplt.kds.plot_lift(
>>>     y_val, y_probas, class_index=1,
>>> );

(Source code, png)

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