.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/calibration/plot_calibration.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_calibration_plot_calibration.py: ====================================== Probability calibration of classifiers ====================================== When performing classification you often want to predict not only the class label, but also the associated probability. This probability gives you some kind of confidence on the prediction. However, not all classifiers provide well-calibrated probabilities, some being over-confident while others being under-confident. Thus, a separate calibration of predicted probabilities is often desirable as a postprocessing. This example illustrates two different methods for this calibration and evaluates the quality of the returned probabilities using Brier's score (see https://en.wikipedia.org/wiki/Brier_score). Compared are the estimated probability using a Gaussian naive Bayes classifier without calibration, with a sigmoid calibration, and with a non-parametric isotonic calibration. One can observe that only the non-parametric model is able to provide a probability calibration that returns probabilities close to the expected 0.5 for most of the samples belonging to the middle cluster with heterogeneous labels. This results in a significantly improved Brier score. .. GENERATED FROM PYTHON SOURCE LINES 25-32 .. code-block:: Python # Authors: # Mathieu Blondel # Alexandre Gramfort # Balazs Kegl # Jan Hendrik Metzen # License: BSD Style. .. GENERATED FROM PYTHON SOURCE LINES 33-35 Generate synthetic dataset -------------------------- .. GENERATED FROM PYTHON SOURCE LINES 35-58 .. code-block:: Python import numpy as np from sklearn.datasets import make_blobs from sklearn.model_selection import train_test_split n_samples = 50000 n_bins = 3 # use 3 bins for calibration_curve as we have 3 clusters here # Generate 3 blobs with 2 classes where the second blob contains # half positive samples and half negative samples. Probability in this # blob is therefore 0.5. centers = [(-5, -5), (0, 0), (5, 5)] X, y = make_blobs(n_samples=n_samples, centers=centers, shuffle=False, random_state=42) y[: n_samples // 2] = 0 y[n_samples // 2 :] = 1 sample_weight = np.random.RandomState(42).rand(y.shape[0]) # split train, test for calibration X_train, X_test, y_train, y_test, sw_train, sw_test = train_test_split( X, y, sample_weight, test_size=0.9, random_state=42 ) .. GENERATED FROM PYTHON SOURCE LINES 59-61 Gaussian Naive-Bayes -------------------- .. GENERATED FROM PYTHON SOURCE LINES 61-91 .. code-block:: Python from sklearn.calibration import CalibratedClassifierCV from sklearn.metrics import brier_score_loss from sklearn.naive_bayes import GaussianNB # With no calibration clf = GaussianNB() clf.fit(X_train, y_train) # GaussianNB itself does not support sample-weights prob_pos_clf = clf.predict_proba(X_test)[:, 1] # With isotonic calibration clf_isotonic = CalibratedClassifierCV(clf, cv=2, method="isotonic") clf_isotonic.fit(X_train, y_train, sample_weight=sw_train) prob_pos_isotonic = clf_isotonic.predict_proba(X_test)[:, 1] # With sigmoid calibration clf_sigmoid = CalibratedClassifierCV(clf, cv=2, method="sigmoid") clf_sigmoid.fit(X_train, y_train, sample_weight=sw_train) prob_pos_sigmoid = clf_sigmoid.predict_proba(X_test)[:, 1] print("Brier score losses: (the smaller the better)") clf_score = brier_score_loss(y_test, prob_pos_clf, sample_weight=sw_test) print("No calibration: %1.3f" % clf_score) clf_isotonic_score = brier_score_loss(y_test, prob_pos_isotonic, sample_weight=sw_test) print("With isotonic calibration: %1.3f" % clf_isotonic_score) clf_sigmoid_score = brier_score_loss(y_test, prob_pos_sigmoid, sample_weight=sw_test) print("With sigmoid calibration: %1.3f" % clf_sigmoid_score) .. rst-class:: sphx-glr-script-out .. code-block:: none Brier score losses: (the smaller the better) No calibration: 0.104 With isotonic calibration: 0.084 With sigmoid calibration: 0.109 .. GENERATED FROM PYTHON SOURCE LINES 92-94 Plot data and the predicted probabilities ----------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 94-145 .. code-block:: Python import matplotlib.pyplot as plt from matplotlib import cm plt.figure() y_unique = np.unique(y) colors = cm.rainbow(np.linspace(0.0, 1.0, y_unique.size)) for this_y, color in zip(y_unique, colors): this_X = X_train[y_train == this_y] this_sw = sw_train[y_train == this_y] plt.scatter( this_X[:, 0], this_X[:, 1], s=this_sw * 50, c=color[np.newaxis, :], alpha=0.5, edgecolor="k", label="Class %s" % this_y, ) plt.legend(loc="best") plt.title("Data") plt.figure() order = np.lexsort((prob_pos_clf,)) plt.plot(prob_pos_clf[order], "r", label="No calibration (%1.3f)" % clf_score) plt.plot( prob_pos_isotonic[order], "g", linewidth=3, label="Isotonic calibration (%1.3f)" % clf_isotonic_score, ) plt.plot( prob_pos_sigmoid[order], "b", linewidth=3, label="Sigmoid calibration (%1.3f)" % clf_sigmoid_score, ) plt.plot( np.linspace(0, y_test.size, 51)[1::2], y_test[order].reshape(25, -1).mean(1), "k", linewidth=3, label=r"Empirical", ) plt.ylim([-0.05, 1.05]) plt.xlabel("Instances sorted according to predicted probability (uncalibrated GNB)") plt.ylabel("P(y=1)") plt.legend(loc="upper left") plt.title("Gaussian naive Bayes probabilities") plt.show() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/calibration/images/sphx_glr_plot_calibration_001.png :alt: Data :srcset: /auto_examples/calibration/images/sphx_glr_plot_calibration_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/calibration/images/sphx_glr_plot_calibration_002.png :alt: Gaussian naive Bayes probabilities :srcset: /auto_examples/calibration/images/sphx_glr_plot_calibration_002.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.369 seconds) .. _sphx_glr_download_auto_examples_calibration_plot_calibration.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/calibration/plot_calibration.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/calibration/plot_calibration.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_calibration.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_calibration.py ` .. include:: plot_calibration.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_