.. _example_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.




.. image:: images/plot_ensemble_oob_001.png
    :align: center




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

.. literalinclude:: plot_ensemble_oob.py
    :lines: 22-

**Total running time of the example:**  9.48 seconds
( 0 minutes  9.48 seconds)