report

scikitplot.kds.report(y_true, y_probas, *, class_index=1, term_tables=True, digits=3, ax=None, figsize=(12, 7), title_fontsize='large', text_fontsize='medium', plot_style=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_true (array-like, shape (n_samples,)) – Ground truth (correct) target values.

• y_probas (array-like, shape (n_samples, n_classes)) – Prediction probabilities for each class returned by a classifier.

• class_index (int, optional, default=1) – Index of the class of interest for multi-class classification. Ignored for binary classification.

• term_tables (bool, optional, default=True) –

  If True, prints a legend for the abbreviations of decile table column names.

  Added in version 0.3.9.

• ax (matplotlib.axes.Axes, optional, default=None) – The axes upon which to plot. If None, a new set of axes is created.

• figsize (tuple of int, optional, default=None) – Tuple denoting figure size of the plot (e.g., (6, 6)).

• title_fontsize (str or int, optional, default='large') – Font size for the plot title. Use e.g., “small”, “medium”, “large” or integer-values.

• text_fontsize (str or int, optional, default='medium') – Font size for the text in the plot. Use e.g., “small”, “medium”, “large” or integer-values.

• digits (int, 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_style (str, 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:

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

Return type:

pandas.DataFrame

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	26.0	3.0	18.4	29	89.655	29.0	26.0	29	3.0	10.175	14.130	15.761	2.970	11.160	1.389
1	2	0.923	0.923	0.923	28.0	25.0	3.0	18.4	28	89.286	57.0	51.0	57	6.0	20.000	27.717	30.978	5.941	21.776	1.386
2	3	0.923	0.923	0.923	29.0	27.0	2.0	18.4	29	93.103	86.0	78.0	86	8.0	30.175	42.391	46.739	7.921	34.470	1.405
3	4	0.923	0.923	0.923	28.0	23.0	5.0	18.4	28	82.143	114.0	101.0	114	13.0	40.000	54.891	61.957	12.871	42.020	1.372
4	5	0.923	0.923	0.923	29.0	26.0	3.0	18.4	29	89.655	143.0	127.0	143	16.0	50.175	69.022	77.717	15.842	53.180	1.376
5	6	0.923	0.923	0.923	28.0	26.0	2.0	18.4	28	92.857	171.0	153.0	171	18.0	60.000	83.152	92.935	17.822	65.330	1.386
6	7	0.049	0.923	0.833	29.0	24.0	5.0	18.4	13	82.759	200.0	177.0	184	23.0	70.175	96.196	100.000	22.772	73.424	1.371
7	8	0.049	0.049	0.049	28.0	2.0	26.0	18.4	0	7.143	228.0	179.0	184	49.0	80.000	97.283	100.000	48.515	48.768	1.216
8	9	0.049	0.049	0.049	29.0	1.0	28.0	18.4	0	3.448	257.0	180.0	184	77.0	90.175	97.826	100.000	76.238	21.588	1.085
9	10	0.049	0.049	0.049	28.0	4.0	24.0	18.4	0	14.286	285.0	184.0	184	101.0	100.000	100.000	100.000	100.000	0.000	1.000