

.. _example_ensemble_plot_forest_iris.py:


====================================================================
Plot the decision surfaces of ensembles of trees on the iris dataset
====================================================================

Plot the decision surfaces of forests of randomized trees trained on pairs of
features of the iris dataset.

This plot compares the decision surfaces learned by a decision tree classifier
(first column), by a random forest classifier (second column), by an extra-
trees classifier (third column) and by an AdaBoost classifier (fourth column).

In the first row, the classifiers are built using the sepal width and the sepal
length features only, on the second row using the petal length and sepal length
only, and on the third row using the petal width and the petal length only.

In descending order of quality, when trained (outside of this example) on all
4 features using 30 estimators and scored using 10 fold cross validation, we see::

    ExtraTreesClassifier()  # 0.95 score
    RandomForestClassifier()  # 0.94 score
    AdaBoost(DecisionTree(max_depth=3))  # 0.94 score
    DecisionTree(max_depth=None)  # 0.94 score

Increasing `max_depth` for AdaBoost lowers the standard deviation of the scores (but
the average score does not improve).

See the console's output for further details about each model.

In this example you might try to:

1) vary the ``max_depth`` for the ``DecisionTreeClassifier`` and
   ``AdaBoostClassifier``, perhaps try ``max_depth=3`` for the
   ``DecisionTreeClassifier`` or ``max_depth=None`` for ``AdaBoostClassifier``
2) vary ``n_estimators``

It is worth noting that RandomForests and ExtraTrees can be fitted in parallel
on many cores as each tree is built independently of the others. AdaBoost's
samples are built sequentially and so do not use multiple cores.


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

.. literalinclude:: plot_forest_iris.py
    :lines: 41-
    