Decision trees are frequently used to represent workflows or algorithms. They also form a method for nonparametric supervised learning. A tree mapping observations to target values is learned on a training set and gives the outcomes of new observations.

Random forests are ensembles of decision trees. Multiple decision trees are trained and aggregated to form a model that is more performant than any of the individual trees. This general idea is the purpose of ensemble learning.

There are many types of ensemble methods. Random forests are an instance of bootstrap aggregating, also called bagging, where models are trained on randomly drawn subsets of the training set.

Random forests yield information about the importance of each feature for the classification or regression task. In this recipe, we will find the most influential features of Boston house prices using a classic dataset that contains a range of diverse indicators...