report

scikitplot.kds.report(y_true, y_probas, *, pos_label=None, class_index=1, display_term_tables=True, digits=3, ax=None, fig=None, figsize=(12, 7), title_fontsize='large', text_fontsize='medium', plot_style=None, data=None, **kwargs)

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.

axmatplotlib.axes.Axes, optional, default=None

The axes upon which to plot. If None, a new set of axes is created.

figsizetuple of int, optional, default=None

Tuple denoting figure size of the plot (e.g., (6, 6)).

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.

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 0.3.9.

Returns:

pandas.DataFrame

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

See also

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.

print_labels

A legend for the abbreviations of decile table column names.

decile_table

Generates the Decile Table 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

	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

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