plot_lift_decile_wise#

scikitplot.kds.plot_lift_decile_wise(y_true, y_probas, *, pos_label=None, class_index=1, title='Decile-wise Lift Plot', title_fontsize='large', text_fontsize='medium', data=None, **kwargs)[source]#

Generates the Decile-wise 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, shape (n_samples,): Ground truth (correct) target values.
y_probasarray-like, shape (n_samples, n_classes): Prediction probabilities for each class returned by a classifier.
titlestr, optional, default=’Decile-wise Lift Plot’: Title of the generated plot.
title_fontsizestr or int, optional, default=14: Font size for the plot title. Use e.g., “small”, “medium”, “large” or integer-values (8, 10, 12, etc.).
text_fontsizestr or int, optional, default=10: Font size for the text in the plot. Use e.g., “small”, “medium”, “large” or integer-values (8, 10, 12, etc.).
**kwargsdict, optional: Generic keyword arguments.

Returns:

axmatplotlib.axes.Axes: The axes with the plotted Decile-wise Lift curves.

Other Parameters:

axmatplotlib.axes.Axes, optional, default=None: The axis to plot the figure on. If None is passed in the current axes will be used (or generated if required).
figmatplotlib.pyplot.figure, optional, default: None: The figure to plot the Visualizer on. If None is passed in the current plot will be used (or generated if required).
figsizetuple, optional, default=None: Width, height in inches. Tuple denoting figure size of the plot e.g. (12, 5)
nrowsint, optional, default=1: Number of rows in the subplot grid.
ncolsint, optional, default=1: Number of columns in the subplot grid.
plot_stylestr, optional, default=None: Check available styles with “plt.style.available”. Examples include: [‘ggplot’, ‘seaborn’, ‘bmh’, ‘classic’, ‘dark_background’, ‘fivethirtyeight’, ‘grayscale’, ‘seaborn-bright’, ‘seaborn-colorblind’, ‘seaborn-dark’, ‘seaborn-dark-palette’, ‘tableau-colorblind10’, ‘fast’].

Added in version 0.4.0.
show_figbool, default=True: Show the plot.
save_figbool, default=False: Save the plot.
save_fig_filenamestr, optional, default=’’: Specify the path and filetype to save the plot. If nothing specified, the plot will be saved as png inside result_images under to the current working directory. Defaults to plot image named to used func.__name__.
verbosebool, optional: If True, prints debugging information.

See also

print_labels: A legend for the abbreviations of decile table column names.
decile_table: Generates the Decile Table from labels and probabilities.
plot_lift: Generates the Decile based cumulative Lift Plot from labels and probabilities.
plot_lift_decile_wise: Generates the Decile-wise Lift Plot from labels and probabilities.
plot_cumulative_gain: Generates the cumulative Gain Plot from labels and probabilities.
plot_ks_statistic: Generates the Kolmogorov-Smirnov (KS) Statistic Plot from labels and probabilities.

References

[1] tensorbored/kds

Examples

>>> import scikitplot as skplt
>>> from sklearn.datasets import load_iris
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.tree import DecisionTreeClassifier
>>> X, y = load_iris(return_X_y=True)
>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, test_size=0.5, random_state=0
... )
>>> clf = DecisionTreeClassifier(max_depth=1, random_state=0)
>>> clf = clf.fit(X_train, y_train)
>>> y_prob = clf.predict_proba(X_test)
>>> skplt.kds.plot_lift_decile_wise(y_test, y_prob, class_index=1)

(Source code, png)

plot_lift_decile_wise#

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