.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/ensemble/plot_ensemble_oob.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_ensemble_oob.py>`
        to download the full example code. or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py:


=============================
OOB Errors for Random Forests
=============================

The ``RandomForestClassifier`` is trained using *bootstrap aggregation*, where
each new tree is fit from a bootstrap sample of the training observations
:math:`z_i = (x_i, y_i)`. The *out-of-bag* (OOB) error is the average error for
each :math:`z_i` calculated using predictions from the trees that do not
contain :math:`z_i` in their respective bootstrap sample. This allows the
``RandomForestClassifier`` to be fit and validated whilst being trained [1]_.

The example below demonstrates how the OOB error can be measured at the
addition of each new tree during training. The resulting plot allows a
practitioner to approximate a suitable value of ``n_estimators`` at which the
error stabilizes.

.. [1] T. Hastie, R. Tibshirani and J. Friedman, "Elements of Statistical
       Learning Ed. 2", p592-593, Springer, 2009.

.. GENERATED FROM PYTHON SOURCE LINES 22-103



.. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_ensemble_oob_001.png
   :alt: plot ensemble oob
   :srcset: /auto_examples/ensemble/images/sphx_glr_plot_ensemble_oob_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    # Authors: The scikit-learn developers
    # SPDX-License-Identifier: BSD-3-Clause

    from collections import OrderedDict

    import matplotlib.pyplot as plt

    from sklearn.datasets import make_classification
    from sklearn.ensemble import RandomForestClassifier

    RANDOM_STATE = 123

    # Generate a binary classification dataset.
    X, y = make_classification(
        n_samples=500,
        n_features=25,
        n_clusters_per_class=1,
        n_informative=15,
        random_state=RANDOM_STATE,
    )

    # NOTE: Setting the `warm_start` construction parameter to `True` disables
    # support for parallelized ensembles but is necessary for tracking the OOB
    # error trajectory during training.
    ensemble_clfs = [
        (
            "RandomForestClassifier, max_features='sqrt'",
            RandomForestClassifier(
                warm_start=True,
                oob_score=True,
                max_features="sqrt",
                random_state=RANDOM_STATE,
            ),
        ),
        (
            "RandomForestClassifier, max_features='log2'",
            RandomForestClassifier(
                warm_start=True,
                max_features="log2",
                oob_score=True,
                random_state=RANDOM_STATE,
            ),
        ),
        (
            "RandomForestClassifier, max_features=None",
            RandomForestClassifier(
                warm_start=True,
                max_features=None,
                oob_score=True,
                random_state=RANDOM_STATE,
            ),
        ),
    ]

    # Map a classifier name to a list of (<n_estimators>, <error rate>) pairs.
    error_rate = OrderedDict((label, []) for label, _ in ensemble_clfs)

    # Range of `n_estimators` values to explore.
    min_estimators = 15
    max_estimators = 150

    for label, clf in ensemble_clfs:
        for i in range(min_estimators, max_estimators + 1, 5):
            clf.set_params(n_estimators=i)
            clf.fit(X, y)

            # Record the OOB error for each `n_estimators=i` setting.
            oob_error = 1 - clf.oob_score_
            error_rate[label].append((i, oob_error))

    # Generate the "OOB error rate" vs. "n_estimators" plot.
    for label, clf_err in error_rate.items():
        xs, ys = zip(*clf_err)
        plt.plot(xs, ys, label=label)

    plt.xlim(min_estimators, max_estimators)
    plt.xlabel("n_estimators")
    plt.ylabel("OOB error rate")
    plt.legend(loc="upper right")
    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 3.070 seconds)


.. _sphx_glr_download_auto_examples_ensemble_plot_ensemble_oob.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_ensemble_oob.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_ensemble_oob.ipynb <plot_ensemble_oob.ipynb>`

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_ensemble_oob.zip <plot_ensemble_oob.zip>`


.. include:: plot_ensemble_oob.recommendations


.. only:: html

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

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