.. _sphx_glr_auto_examples_ensemble_plot_adaboost_twoclass.py: ================== Two-class AdaBoost ================== This example fits an AdaBoosted decision stump on a non-linearly separable classification dataset composed of two "Gaussian quantiles" clusters (see :func:`sklearn.datasets.make_gaussian_quantiles`) and plots the decision boundary and decision scores. The distributions of decision scores are shown separately for samples of class A and B. The predicted class label for each sample is determined by the sign of the decision score. Samples with decision scores greater than zero are classified as B, and are otherwise classified as A. The magnitude of a decision score determines the degree of likeness with the predicted class label. Additionally, a new dataset could be constructed containing a desired purity of class B, for example, by only selecting samples with a decision score above some value. .. image:: /auto_examples/ensemble/images/sphx_glr_plot_adaboost_twoclass_001.png :align: center .. code-block:: python print(__doc__) # Author: Noel Dawe # # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn.ensemble import AdaBoostClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.datasets import make_gaussian_quantiles # Construct dataset X1, y1 = make_gaussian_quantiles(cov=2., n_samples=200, n_features=2, n_classes=2, random_state=1) X2, y2 = make_gaussian_quantiles(mean=(3, 3), cov=1.5, n_samples=300, n_features=2, n_classes=2, random_state=1) X = np.concatenate((X1, X2)) y = np.concatenate((y1, - y2 + 1)) # Create and fit an AdaBoosted decision tree bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1), algorithm="SAMME", n_estimators=200) bdt.fit(X, y) plot_colors = "br" plot_step = 0.02 class_names = "AB" plt.figure(figsize=(10, 5)) # Plot the decision boundaries plt.subplot(121) x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step), np.arange(y_min, y_max, plot_step)) Z = bdt.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired) plt.axis("tight") # Plot the training points for i, n, c in zip(range(2), class_names, plot_colors): idx = np.where(y == i) plt.scatter(X[idx, 0], X[idx, 1], c=c, cmap=plt.cm.Paired, label="Class %s" % n) plt.xlim(x_min, x_max) plt.ylim(y_min, y_max) plt.legend(loc='upper right') plt.xlabel('x') plt.ylabel('y') plt.title('Decision Boundary') # Plot the two-class decision scores twoclass_output = bdt.decision_function(X) plot_range = (twoclass_output.min(), twoclass_output.max()) plt.subplot(122) for i, n, c in zip(range(2), class_names, plot_colors): plt.hist(twoclass_output[y == i], bins=10, range=plot_range, facecolor=c, label='Class %s' % n, alpha=.5) x1, x2, y1, y2 = plt.axis() plt.axis((x1, x2, y1, y2 * 1.2)) plt.legend(loc='upper right') plt.ylabel('Samples') plt.xlabel('Score') plt.title('Decision Scores') plt.tight_layout() plt.subplots_adjust(wspace=0.35) plt.show() **Total running time of the script:** (0 minutes 4.682 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`plot_adaboost_twoclass.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`plot_adaboost_twoclass.ipynb `