scikit-learn implements both conventional Random Forest trees, as well as Extra Trees. In this section, we will provide basic regression and classification examples with both algorithms, using the scikit-learn implementations.
Using scikit-learn
Random forests for classification
The Random Forests classification class is implemented in RandomForestClassifier, under the sklearn.ensemble package. It has a number of parameters, such as the ensemble's size, the maximum tree depth, the number of samples required to make or split a node, and many more.
In this example, we will try to classify the hand-written digits dataset, using the Random Forest classification ensemble. As usual, we load the required classes and data and...
