Before we begin with the Regression Trees or the Decision Tree Regression, let’s recall and review the simple Decision Tree.
Decision Tree is a supervised machine learning algorithm and it is one of the popular machine learning algorithm. It is a tree like structure constructed on the basis of attributes/features . Decision Trees is the non-parametric supervised learning approach.
So guys you may have heard the term CART which stands for Classification and Regression Trees. In my pervious blog of Decision Tree,I have covered basics of Decision Tree and Classification tree. Regression Trees are bit more complex than classification tree.
Decision Tree Regression is a Non- Linear Regression technique.
Decision Tree Regression model is in the form of a tree structure.It breaks down a data set into smaller and smaller subsets while at the same time an associated decision tree is developed.
Decision Tree Regression is used to predict the target variable whose values are continous in nature. Regression Tree can easily handle the complicated data.
Impurity Measure for regression tree are :
- Least Square: Least squares regression is a way to find the line of best fit for a set of data. It does this by creating a model that minimizes the sum of the squared…
