.. _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. .. image:: images/plot_forest_iris_001.png :align: center **Script output**:: DecisionTree with features [0, 1] has a score of 0.926666666667 RandomForest with 30 estimators with features [0, 1] has a score of 0.926666666667 ExtraTrees with 30 estimators with features [0, 1] has a score of 0.926666666667 AdaBoost with 30 estimators with features [0, 1] has a score of 0.86 DecisionTree with features [0, 2] has a score of 0.993333333333 RandomForest with 30 estimators with features [0, 2] has a score of 0.993333333333 ExtraTrees with 30 estimators with features [0, 2] has a score of 0.993333333333 AdaBoost with 30 estimators with features [0, 2] has a score of 0.993333333333 DecisionTree with features [2, 3] has a score of 0.993333333333 RandomForest with 30 estimators with features [2, 3] has a score of 0.993333333333 ExtraTrees with 30 estimators with features [2, 3] has a score of 0.993333333333 AdaBoost with 30 estimators with features [2, 3] has a score of 0.993333333333 **Python source code:** :download:`plot_forest_iris.py ` .. literalinclude:: plot_forest_iris.py :lines: 41- **Total running time of the example:** 6.76 seconds ( 0 minutes 6.76 seconds)