.. _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.

The logistic regression is not a multiclass classifier out of the box. As
a result it can identify only the first class.




.. image:: /auto_examples/classification/images/sphx_glr_plot_classification_probability_001.png
    :align: center


.. rst-class:: sphx-glr-script-out

 Out::

    classif_rate for L1 logistic : 79.333333 
    classif_rate for L2 logistic (OvR) : 76.666667 
    classif_rate for Linear SVC : 82.000000 
    classif_rate for L2 logistic (Multinomial) : 82.000000 
    classif_rate for GPC : 82.666667




|


.. code-block:: python

    print(__doc__)

    # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
    # License: BSD 3 clause

    import matplotlib.pyplot as plt
    import numpy as np

    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 = 1.0
    kernel = 1.0 * RBF([1.0, 1.0])  # for GPC

    # Create different classifiers. The logistic regression cannot do
    # multiclass out of the box.
    classifiers = {'L1 logistic': LogisticRegression(C=C, penalty='l1'),
                   'L2 logistic (OvR)': LogisticRegression(C=C, penalty='l2'),
                   'Linear SVC': SVC(kernel='linear', C=C, probability=True,
                                     random_state=0),
                   'L2 logistic (Multinomial)': LogisticRegression(
                    C=C, solver='lbfgs', multi_class='multinomial'),
                   '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)
        classif_rate = np.mean(y_pred.ravel() == y.ravel()) * 100
        print("classif_rate for %s : %f " % (name, classif_rate))

        # 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='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  1.360 seconds)



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     :download:`Download Python source code: plot_classification_probability.py <plot_classification_probability.py>`



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