.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_classification_plot_classification_probability.py: =============================== Plot classification probability =============================== Plot the classification probability for different classifiers. We use a 3 class dataset, and we classify it with a Support Vector classifier, L1 and L2 penalized logistic regression with either a One-Vs-Rest or multinomial setting, and Gaussian process classification. Linear SVC is not a probabilistic classifier by default but it has a built-in calibration option enabled in this example (`probability=True`). The logistic regression with One-Vs-Rest is not a multiclass classifier out of the box. As a result it has more trouble in separating class 2 and 3 than the other estimators. .. image:: /auto_examples/classification/images/sphx_glr_plot_classification_probability_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Accuracy (train) for L1 logistic: 83.3% Accuracy (train) for L2 logistic (Multinomial): 82.7% Accuracy (train) for L2 logistic (OvR): 79.3% Accuracy (train) for Linear SVC: 82.0% Accuracy (train) for GPC: 82.7% | .. code-block:: python print(__doc__) # Author: Alexandre Gramfort # License: BSD 3 clause import matplotlib.pyplot as plt import numpy as np from sklearn.metrics import accuracy_score from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.gaussian_process import GaussianProcessClassifier from sklearn.gaussian_process.kernels import RBF from sklearn import datasets iris = datasets.load_iris() X = iris.data[:, 0:2] # we only take the first two features for visualization y = iris.target n_features = X.shape[1] C = 10 kernel = 1.0 * RBF([1.0, 1.0]) # for GPC # Create different classifiers. classifiers = { 'L1 logistic': LogisticRegression(C=C, penalty='l1', solver='saga', multi_class='multinomial', max_iter=10000), 'L2 logistic (Multinomial)': LogisticRegression(C=C, penalty='l2', solver='saga', multi_class='multinomial', max_iter=10000), 'L2 logistic (OvR)': LogisticRegression(C=C, penalty='l2', solver='saga', multi_class='ovr', max_iter=10000), 'Linear SVC': SVC(kernel='linear', C=C, probability=True, random_state=0), 'GPC': GaussianProcessClassifier(kernel) } n_classifiers = len(classifiers) plt.figure(figsize=(3 * 2, n_classifiers * 2)) plt.subplots_adjust(bottom=.2, top=.95) xx = np.linspace(3, 9, 100) yy = np.linspace(1, 5, 100).T xx, yy = np.meshgrid(xx, yy) Xfull = np.c_[xx.ravel(), yy.ravel()] for index, (name, classifier) in enumerate(classifiers.items()): classifier.fit(X, y) y_pred = classifier.predict(X) accuracy = accuracy_score(y, y_pred) print("Accuracy (train) for %s: %0.1f%% " % (name, accuracy * 100)) # View probabilities: probas = classifier.predict_proba(Xfull) n_classes = np.unique(y_pred).size for k in range(n_classes): plt.subplot(n_classifiers, n_classes, index * n_classes + k + 1) plt.title("Class %d" % k) if k == 0: plt.ylabel(name) imshow_handle = plt.imshow(probas[:, k].reshape((100, 100)), extent=(3, 9, 1, 5), origin='lower') plt.xticks(()) plt.yticks(()) idx = (y_pred == k) if idx.any(): plt.scatter(X[idx, 0], X[idx, 1], marker='o', c='w', edgecolor='k') ax = plt.axes([0.15, 0.04, 0.7, 0.05]) plt.title("Probability") plt.colorbar(imshow_handle, cax=ax, orientation='horizontal') plt.show() **Total running time of the script:** ( 0 minutes 1.409 seconds) .. _sphx_glr_download_auto_examples_classification_plot_classification_probability.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: plot_classification_probability.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_classification_probability.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_