report#

scikitplot.kds.report(y_true, y_probas, *, pos_label=None, class_index=1, display_term_tables=True, digits=3, title_fontsize='large', text_fontsize='medium', plot_style=None, figsize=(10, 5), nrows=2, ncols=2, data=None, **kwargs)[source]#

Generates a decile table and four plots:

Lift -> plot_lift
Lift@Decile -> plot_lift_decile_wise
Gain -> plot_cumulative_gain
KS -> plot_ks_statistic

from labels and probabilities.

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.
class_indexint, optional, default=1: Index of the class of interest for multi-class classification. Ignored for binary classification.
labelsbool, optional, default=True: If True, prints a legend for the abbreviations of decile table column names.

Deprecated since version 0.3.9: This parameter is deprecated and will be removed in version 0.5.0. Use display_term_tables instead.
display_term_tablesbool, optional, default=True: If True, prints a legend for the abbreviations of decile table column names.

Added in version 0.3.9.
title_fontsizestr or int, optional, default=’large’: Font size for the plot title. Use e.g., “small”, “medium”, “large” or integer-values.
text_fontsizestr or int, optional, default=’medium’: Font size for the text in the plot. Use e.g., “small”, “medium”, “large” or integer-values.
digitsint, optional, default=3: Number of digits for formatting output floating point values. Use e.g., 2 or 4.

Added in version 0.3.9.
**kwargsdict, optional: Generic keyword arguments.

Returns:

pandas.DataFrame: The dataframe containing the decile table with the deciles and related information.

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).

Added in version 0.4.0.

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).

Added in version 0.4.0.

figsizetuple, optional, default=None

Width, height in inches. Tuple denoting figure size of the plot e.g. (12, 5)

Added in version 0.4.0.

nrowsint, optional, default=1

Number of rows in the subplot grid.

Added in version 0.4.0.

ncolsint, optional, default=1

Number of columns in the subplot grid.

Added in version 0.4.0.

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.

Added in version 0.4.0.

save_figbool, default=False

Save the plot.

Added in version 0.4.0.

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__.

Added in version 0.4.0.

overwritebool, optional, default=True

If False and a file exists, auto-increments the filename to avoid overwriting.

Added in version 0.4.0.

add_timestampbool, optional, default=False

Whether to append a timestamp to the filename. Default is False.

Added in version 0.4.0.

verbosebool, optional

If True, enables verbose output with informative messages during execution. Useful for debugging or understanding internal operations such as backend selection, font loading, and file saving status. If False, runs silently unless errors occur.

Default is False.

Added in version 0.4.0: The verbose parameter was added to control logging and user feedback verbosity.

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

>>> from sklearn.datasets import (
...     load_breast_cancer as data_2_classes,
... )
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.tree import DecisionTreeClassifier
>>> import scikitplot as skplt
>>> X, y = data_2_classes(return_X_y=True, as_frame=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).fit(
...     X_train, y_train
... )
>>> y_prob = clf.predict_proba(X_test)
>>> dt = skplt.kds.report(
>>>     y_test, y_prob, class_index=1
>>> )
>>> dt

LABELS INFO:

 prob_min         : Minimum probability in a particular decile
 prob_max         : Minimum probability in a particular decile
 prob_avg         : Average probability in a particular decile
 cnt_events       : Count of events in a particular decile
 cnt_resp         : Count of responders in a particular decile
 cnt_non_resp     : Count of non-responders in a particular decile
 cnt_resp_rndm    : Count of responders if events assigned randomly in a particular decile
 cnt_resp_wiz     : Count of best possible responders in a particular decile
 resp_rate        : Response Rate in a particular decile [(cnt_resp/cnt_cust)*100]
 cum_events       : Cumulative sum of events decile-wise 
 cum_resp         : Cumulative sum of responders decile-wise 
 cum_resp_wiz     : Cumulative sum of best possible responders decile-wise 
 cum_non_resp     : Cumulative sum of non-responders decile-wise 
 cum_events_pct   : Cumulative sum of percentages of events decile-wise 
 cum_resp_pct     : Cumulative sum of percentages of responders decile-wise 
 cum_resp_pct_wiz : Cumulative sum of percentages of best possible responders decile-wise 
 cum_non_resp_pct : Cumulative sum of percentages of non-responders decile-wise 
 KS               : KS Statistic decile-wise 
 lift             : Cumuative Lift Value decile-wise

../../_images/scikitplot.kds.report_0_1.png

	decile	prob_min	prob_max	prob_avg	cnt_cust	cnt_resp	cnt_non_resp	cnt_resp_rndm	cnt_resp_wiz	resp_rate	cum_cust	cum_resp	cum_resp_wiz	cum_non_resp	cum_cust_pct	cum_resp_pct	cum_resp_pct_wiz	cum_non_resp_pct	KS	lift
0	1	0.923	0.923	0.923	29.0	29.0	0.0	18.4	NaN	100.000	29.0	29.0	NaN	0.0	10.175	15.761	NaN	0.000	15.761	1.549
1	2	0.923	0.923	0.923	28.0	25.0	3.0	18.4	29.0	89.286	57.0	54.0	29.0	3.0	20.000	29.348	15.761	2.970	26.378	1.467
2	3	0.923	0.923	0.923	29.0	26.0	3.0	18.4	28.0	89.655	86.0	80.0	57.0	6.0	30.175	43.478	30.978	5.941	37.537	1.441
3	4	0.923	0.923	0.923	28.0	24.0	4.0	18.4	29.0	85.714	114.0	104.0	86.0	10.0	40.000	56.522	46.739	9.901	46.621	1.413
4	5	0.923	0.923	0.923	29.0	28.0	1.0	18.4	28.0	96.552	143.0	132.0	114.0	11.0	50.175	71.739	61.957	10.891	60.848	1.430
5	6	0.923	0.923	0.923	28.0	26.0	2.0	18.4	29.0	92.857	171.0	158.0	143.0	13.0	60.000	85.870	77.717	12.871	72.999	1.431
6	7	0.049	0.923	0.833	29.0	19.0	10.0	18.4	28.0	65.517	200.0	177.0	171.0	23.0	70.175	96.196	92.935	22.772	73.424	1.371
7	8	0.049	0.049	0.049	28.0	6.0	22.0	18.4	13.0	21.429	228.0	183.0	184.0	45.0	80.000	99.457	100.000	44.554	54.903	1.243
8	9	0.049	0.049	0.049	29.0	1.0	28.0	18.4	0.0	3.448	257.0	184.0	184.0	73.0	90.175	100.000	100.000	72.277	27.723	1.109
9	10	0.049	0.049	0.049	28.0	0.0	28.0	18.4	0.0	0.000	285.0	184.0	184.0	101.0	100.000	100.000	100.000	100.000	0.000	1.000

report#

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