.. 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 <sphx_glr_download_auto_examples_ensemble_plot_adaboost_twoclass.py>`
        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/runner/work/scikit-learn-pst/scikit-learn-pst/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 <noel.dawe@gmail.com>
    #
    # 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 0.624 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 <plot_adaboost_twoclass.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

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


.. include:: plot_adaboost_twoclass.recommendations


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_