decile_table#

scikitplot.kds.decile_table(y_true, y_probas, *, class_index=1, labels=True, change_deciles=10, digits=3, **kwargs)#

Generates the Decile Table from labels and probabilities

The Decile Table is creared by first sorting the customers by their predicted probabilities, in decreasing order from highest (closest to one) to lowest (closest to zero). Splitting the customers into equally sized segments, we create groups containing the same numbers of customers, for example, 10 decile groups each containing 10% of the customer base.

Parameters:

  • y_true (array-like, shape (n_samples,)) – Ground truth (correct/actual) target values.

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

  • class_index (int, optional, default=1) –

    Target class.

    Added in version 0.3.9.

  • labels (bool, optional, default=True) – If True, prints a legend for the abbreviations of decile table column names.

  • change_deciles (int, optional, default=10) – The number of partitions for creating the table. Defaults to 10 for deciles.

  • digits (int, optional, default=3) –

    The decimal precision for the result.

    Added in version 0.3.9.

Returns:

The dataframe dt (decile-table) with the deciles and related information.

Return type:

pd.DataFrame

See also

print_labels

A legend for the abbreviations of decile table column names.

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)
>>> skplt.kds.decile_table(
>>>     y_test, y_prob, class_index=1
>>> )

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
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