.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/ensemble/plot_adaboost_twoclass.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_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. .. GENERATED FROM PYTHON SOURCE LINES 19-112 .. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_adaboost_twoclass_001.png :alt: Decision Boundary, Decision Scores :srcset: /auto_examples/ensemble/images/sphx_glr_plot_adaboost_twoclass_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/circleci/project/examples/ensemble/plot_adaboost_twoclass.py:73: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored | .. code-block:: Python # Author: Noel Dawe # # License: BSD 3 clause import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import make_gaussian_quantiles from sklearn.ensemble import AdaBoostClassifier from sklearn.inspection import DecisionBoundaryDisplay from sklearn.tree import DecisionTreeClassifier # Construct dataset X1, y1 = make_gaussian_quantiles( cov=2.0, 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 ax = plt.subplot(121) disp = DecisionBoundaryDisplay.from_estimator( bdt, X, cmap=plt.cm.Paired, response_method="predict", ax=ax, xlabel="x", ylabel="y", ) x_min, x_max = disp.xx0.min(), disp.xx0.max() y_min, y_max = disp.xx1.min(), disp.xx1.max() 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, s=20, edgecolor="k", label="Class %s" % n, ) plt.xlim(x_min, x_max) plt.ylim(y_min, y_max) plt.legend(loc="upper right") 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=0.5, edgecolor="k", ) 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() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.788 seconds) .. _sphx_glr_download_auto_examples_ensemble_plot_adaboost_twoclass.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/ensemble/plot_adaboost_twoclass.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/ensemble/plot_adaboost_twoclass.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_adaboost_twoclass.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_adaboost_twoclass.py ` .. include:: plot_adaboost_twoclass.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_