plot_report with examples#

An example showing the report function with a scikit-learn classifier (e.g., LogisticRegression) instance.

# Authors: The scikit-plots developers
# SPDX-License-Identifier: BSD-3-Clause

from sklearn.datasets import (
    load_breast_cancer as data_2_classes,
    # load_iris as data_3_classes,
)
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

# importing pylab or pyplot
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(0)  # reproducibility

# Import scikit-plot
import scikitplot.decile as sp

# Load the data
X, y = data_2_classes(return_X_y=True, as_frame=False)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.5, random_state=0)

# Create an instance of the LogisticRegression
model = LogisticRegression(max_iter=int(1e5), random_state=0).fit(X_train, y_train)

# Perform predictions
y_val_prob = model.predict_proba(X_val)

sp.kds.decile_table(
    y_val,
    y_val_prob,
    save_fig=True,
    save_fig_filename="",
    # overwrite=True,
    add_timestamp=True,
    # verbose=True,
    display_term_tables=True,
)

	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	0.999898	0.999998	0.999953	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	0.999395	0.999897	0.999689	28.0	28.0	0.0	18.4	28	100.000000	57.0	57.0	57	0.0	20.000000	30.978261	30.978261	0.000000	30.978261	1.548913
2	3	0.997622	0.999376	0.998770	29.0	29.0	0.0	18.4	29	100.000000	86.0	86.0	86	0.0	30.175439	46.739130	46.739130	0.000000	46.739130	1.548913
3	4	0.992830	0.997497	0.996062	28.0	28.0	0.0	18.4	28	100.000000	114.0	114.0	114	0.0	40.000000	61.956522	61.956522	0.000000	61.956522	1.548913
4	5	0.959560	0.992494	0.980356	29.0	29.0	0.0	18.4	29	100.000000	143.0	143.0	143	0.0	50.175439	77.717391	77.717391	0.000000	77.717391	1.548913
5	6	0.771810	0.955756	0.888427	28.0	25.0	3.0	18.4	28	89.285714	171.0	168.0	171	3.0	60.000000	91.304348	92.934783	2.970297	88.334051	1.521739
6	7	0.065823	0.769488	0.346444	29.0	16.0	13.0	18.4	13	55.172414	200.0	184.0	184	16.0	70.175439	100.000000	100.000000	15.841584	84.158416	1.425000
7	8	0.000369	0.048060	0.010959	28.0	0.0	28.0	18.4	0	0.000000	228.0	184.0	184	44.0	80.000000	100.000000	100.000000	43.564356	56.435644	1.250000
8	9	0.000001	0.000349	0.000057	29.0	0.0	29.0	18.4	0	0.000000	257.0	184.0	184	73.0	90.175439	100.000000	100.000000	72.277228	27.722772	1.108949
9	10	0.000000	0.000001	0.000000	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

Plot!

ax = sp.kds.report(
    y_val,
    y_val_prob,
    save_fig=True,
    save_fig_filename="",
    # overwrite=True,
    add_timestamp=True,
    # verbose=True,
    display_term_tables=True,
)

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

Tags: model-type: classification model-workflow: model evaluation plot-type: line plot-type: decile level: beginner purpose: showcase

Total running time of the script: (0 minutes 1.270 seconds)

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