1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
|
"""
======================================================
Post-hoc tuning the cut-off point of decision function
======================================================
Once a binary classifier is trained, the :term:`predict` method outputs class label
predictions corresponding to a thresholding of either the :term:`decision_function` or
the :term:`predict_proba` output. The default threshold is defined as a posterior
probability estimate of 0.5 or a decision score of 0.0. However, this default strategy
may not be optimal for the task at hand.
This example shows how to use the
:class:`~sklearn.model_selection.TunedThresholdClassifierCV` to tune the decision
threshold, depending on a metric of interest.
"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
# %%
# The diabetes dataset
# --------------------
#
# To illustrate the tuning of the decision threshold, we will use the diabetes dataset.
# This dataset is available on OpenML: https://www.openml.org/d/37. We use the
# :func:`~sklearn.datasets.fetch_openml` function to fetch this dataset.
from sklearn.datasets import fetch_openml
diabetes = fetch_openml(data_id=37, as_frame=True, parser="pandas")
data, target = diabetes.data, diabetes.target
# %%
# We look at the target to understand the type of problem we are dealing with.
target.value_counts()
# %%
# We can see that we are dealing with a binary classification problem. Since the
# labels are not encoded as 0 and 1, we make it explicit that we consider the class
# labeled "tested_negative" as the negative class (which is also the most frequent)
# and the class labeled "tested_positive" the positive as the positive class:
neg_label, pos_label = target.value_counts().index
# %%
# We can also observe that this binary problem is slightly imbalanced where we have
# around twice more samples from the negative class than from the positive class. When
# it comes to evaluation, we should consider this aspect to interpret the results.
#
# Our vanilla classifier
# ----------------------
#
# We define a basic predictive model composed of a scaler followed by a logistic
# regression classifier.
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
model = make_pipeline(StandardScaler(), LogisticRegression())
model
# %%
# We evaluate our model using cross-validation. We use the accuracy and the balanced
# accuracy to report the performance of our model. The balanced accuracy is a metric
# that is less sensitive to class imbalance and will allow us to put the accuracy
# score in perspective.
#
# Cross-validation allows us to study the variance of the decision threshold across
# different splits of the data. However, the dataset is rather small and it would be
# detrimental to use more than 5 folds to evaluate the dispersion. Therefore, we use
# a :class:`~sklearn.model_selection.RepeatedStratifiedKFold` where we apply several
# repetitions of 5-fold cross-validation.
import pandas as pd
from sklearn.model_selection import RepeatedStratifiedKFold, cross_validate
scoring = ["accuracy", "balanced_accuracy"]
cv_scores = [
"train_accuracy",
"test_accuracy",
"train_balanced_accuracy",
"test_balanced_accuracy",
]
cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=10, random_state=42)
cv_results_vanilla_model = pd.DataFrame(
cross_validate(
model,
data,
target,
scoring=scoring,
cv=cv,
return_train_score=True,
return_estimator=True,
)
)
cv_results_vanilla_model[cv_scores].aggregate(["mean", "std"]).T
# %%
# Our predictive model succeeds to grasp the relationship between the data and the
# target. The training and testing scores are close to each other, meaning that our
# predictive model is not overfitting. We can also observe that the balanced accuracy is
# lower than the accuracy, due to the class imbalance previously mentioned.
#
# For this classifier, we let the decision threshold, used convert the probability of
# the positive class into a class prediction, to its default value: 0.5. However, this
# threshold might not be optimal. If our interest is to maximize the balanced accuracy,
# we should select another threshold that would maximize this metric.
#
# The :class:`~sklearn.model_selection.TunedThresholdClassifierCV` meta-estimator allows
# to tune the decision threshold of a classifier given a metric of interest.
#
# Tuning the decision threshold
# -----------------------------
#
# We create a :class:`~sklearn.model_selection.TunedThresholdClassifierCV` and
# configure it to maximize the balanced accuracy. We evaluate the model using the same
# cross-validation strategy as previously.
from sklearn.model_selection import TunedThresholdClassifierCV
tuned_model = TunedThresholdClassifierCV(estimator=model, scoring="balanced_accuracy")
cv_results_tuned_model = pd.DataFrame(
cross_validate(
tuned_model,
data,
target,
scoring=scoring,
cv=cv,
return_train_score=True,
return_estimator=True,
)
)
cv_results_tuned_model[cv_scores].aggregate(["mean", "std"]).T
# %%
# In comparison with the vanilla model, we observe that the balanced accuracy score
# increased. Of course, it comes at the cost of a lower accuracy score. It means that
# our model is now more sensitive to the positive class but makes more mistakes on the
# negative class.
#
# However, it is important to note that this tuned predictive model is internally the
# same model as the vanilla model: they have the same fitted coefficients.
import matplotlib.pyplot as plt
vanilla_model_coef = pd.DataFrame(
[est[-1].coef_.ravel() for est in cv_results_vanilla_model["estimator"]],
columns=diabetes.feature_names,
)
tuned_model_coef = pd.DataFrame(
[est.estimator_[-1].coef_.ravel() for est in cv_results_tuned_model["estimator"]],
columns=diabetes.feature_names,
)
fig, ax = plt.subplots(ncols=2, figsize=(12, 4), sharex=True, sharey=True)
vanilla_model_coef.boxplot(ax=ax[0])
ax[0].set_ylabel("Coefficient value")
ax[0].set_title("Vanilla model")
tuned_model_coef.boxplot(ax=ax[1])
ax[1].set_title("Tuned model")
_ = fig.suptitle("Coefficients of the predictive models")
# %%
# Only the decision threshold of each model was changed during the cross-validation.
decision_threshold = pd.Series(
[est.best_threshold_ for est in cv_results_tuned_model["estimator"]],
)
ax = decision_threshold.plot.kde()
ax.axvline(
decision_threshold.mean(),
color="k",
linestyle="--",
label=f"Mean decision threshold: {decision_threshold.mean():.2f}",
)
ax.set_xlabel("Decision threshold")
ax.legend(loc="upper right")
_ = ax.set_title(
"Distribution of the decision threshold \nacross different cross-validation folds"
)
# %%
# In average, a decision threshold around 0.32 maximizes the balanced accuracy, which is
# different from the default decision threshold of 0.5. Thus tuning the decision
# threshold is particularly important when the output of the predictive model
# is used to make decisions. Besides, the metric used to tune the decision threshold
# should be chosen carefully. Here, we used the balanced accuracy but it might not be
# the most appropriate metric for the problem at hand. The choice of the "right" metric
# is usually problem-dependent and might require some domain knowledge. Refer to the
# example entitled,
# :ref:`sphx_glr_auto_examples_model_selection_plot_cost_sensitive_learning.py`,
# for more details.
|
