decile_table#

scikitplot.decile.kds.decile_table(y_true, y_score, *, labels=None, class_index=1, pos_label=None, change_deciles=10, digits=6, feature_infos=None, **kwargs)[source]#

Generate 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.

Added in version 0.3.9.

Parameters:
y_truearray-like of shape (n_samples,)
True binary labels. If labels are not either {-1, 1} or {0, 1}, then pos_label should be explicitly given.
y_scorearray-like of shape (n_samples,)
Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by “decision_function” on some classifiers). For decision_function scores, values greater than or equal to zero should indicate the positive class.
pos_labelint, float, bool or str, default=None
The label of the positive class. When pos_label=None, if y_true is in {-1, 1} or {0, 1}, pos_label is set to 1, otherwise an error will be raised.
class_indexint, optional

Index of the class for which to extract probabilities in multi-class case. If None, returns all class probabilities in the 2D case. Ignored if y_score is 1D.

Added in version 0.3.9.

labelsarray-like of shape (n_classes,), default=None
List of labels to index the matrix. This may be used to reorder or select a subset of labels. If None is given, those that appear at least once in y_true or y_pred are used in sorted order.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
change_decilesint, optional, default=10

The number of partitions for creating the table. Defaults to 10 for deciles.

digitsint, optional, default=6

The decimal precision for the result.

Added in version 0.3.9.

feature_infosbool, optional, default=None

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

**kwargsdict, optional

Added in version 0.3.9.

Returns:
pandas.DataFrame

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

>>> from sklearn.datasets import (
...     load_breast_cancer as data_2_classes,
... )
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.tree import DecisionTreeClassifier
...
>>> 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)
...
>>> import scikitplot.decile.kds as kds
>>> kds.decile_table(
>>>     y_test, y_prob, class_index=1
>>> )
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 0.923077 0.923077 0.923077 29.0 29.0 0.0 18.4 29 100.000000 29.0 29.0 29 0.0 10.175439 15.760870 15.760870 0.000000 15.760870 1.548913
1 2 0.923077 0.923077 0.923077 28.0 25.0 3.0 18.4 28 89.285714 57.0 54.0 57 3.0 20.000000 29.347826 30.978261 2.970297 26.377529 1.467391
2 3 0.923077 0.923077 0.923077 29.0 26.0 3.0 18.4 29 89.655172 86.0 80.0 86 6.0 30.175439 43.478261 46.739130 5.940594 37.537667 1.440849
3 4 0.923077 0.923077 0.923077 28.0 24.0 4.0 18.4 28 85.714286 114.0 104.0 114 10.0 40.000000 56.521739 61.956522 9.900990 46.620749 1.413043
4 5 0.923077 0.923077 0.923077 29.0 28.0 1.0 18.4 29 96.551724 143.0 132.0 143 11.0 50.175439 71.739130 77.717391 10.891089 60.848041 1.429766
5 6 0.923077 0.923077 0.923077 28.0 26.0 2.0 18.4 28 92.857143 171.0 158.0 171 13.0 60.000000 85.869565 92.934783 12.871287 72.998278 1.431159
6 7 0.049020 0.923077 0.832657 29.0 19.0 10.0 18.4 13 65.517241 200.0 177.0 184 23.0 70.175439 96.195652 100.000000 22.772277 73.423375 1.370788
7 8 0.049020 0.049020 0.049020 28.0 6.0 22.0 18.4 0 21.428571 228.0 183.0 184 45.0 80.000000 99.456522 100.000000 44.554455 54.902067 1.243207
8 9 0.049020 0.049020 0.049020 29.0 1.0 28.0 18.4 0 3.448276 257.0 184.0 184 73.0 90.175439 100.000000 100.000000 72.277228 27.722772 1.108949
9 10 0.049020 0.049020 0.049020 28.0 0.0 28.0 18.4 0 0.000000 285.0 184.0 184 101.0 100.000000 100.000000 100.000000 100.000000 0.000000 1.000000
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