In a previous article, we learned how to create a Decision Tree for classification. The allow us to predict which cateogory an observation belongs to. In this article, we will learn how to build a tree classifier for regression, using sklearn, which allows us to predict coutinous values (like the cost of a house).
To create a tree model, we use the
DecisionTreeRegressor class. We use this similar to any other model; we create an instance, then pass our x and y data to the
# Load libraries from sklearn.tree import DecisionTreeRegressor from sklearn import datasets boston = datasets.load_boston() features = boston.data target = boston.target decisiontree = DecisionTreeRegressor() model = decisiontree.fit(features, target) print(model.score())