we only focus on decision trees with a regression task. For this, the equivalent Scikit-learn class is DecisionTreeRegressor. We will start by discussing how to train, visualize and make predictions with Decision Trees for a regression task. We will also discuss how to regularize hyperparameters in decision trees.
Different Regression models i.e. Linear Regression, Decision Tree Regression, Gradient Boosted Regression, and Random Forest Regression were used. The performance of those models using R² were compared. Based on these performance score, better performing model were suggested to predict house price.
Decision Tree is one of the most widely used machine learning algorithm. It is a supervised learning algorithm that can perform both classification and regression operations.