Mastering pandas - Second Edition

Random forest

Random forest is an example of a non-parametric model, as are decision trees. Random forests are based on decision trees. The decision boundary is learned from the data itself. It doesn't have to be a line or a polynomial or radial basis function. The random forest model builds upon the decision tree concept by producing a large number of, or a forest of, decision trees. It takes a random sample of the data and identifies a set of features to grow each decision tree. The error rate of the model is compared across sets of decision trees to find the set of features that produce the strongest classification model.

To use a random forest in scikit-learn, we import the RandomForestClassifier module:

from sklearn import RandomForestClassifier ...