Regression trees
Decision trees are used to predict a response or class y from several input variables x1, x2,…,xn. If y is a continuous response, it's called a regression tree, if y is categorical, it's called a classification tree. That's why these methods are often called Classification and Regression Tree (CART). The algorithm is based on the following procedure: at each node of the tree, we check the value of one the input xi and depending of the (binary) answer we continue to the left or to the right branch. When we reach a leaf we will find the prediction.
This algorithm starts from grouped data into a single node (root node) and executes a comprehensive recursion of all possible subdivisions at every step. At each step, the best subdivision is chosen, that is, the one that produces as many homogeneous branches as possible.
In the regression trees, we try to partition the data space into small-enough parts where we can apply a simple different model on each part. The non leaf part of...
