Regression tree

Information

• A regression tree is a decision tree used in regression problems, i.e., where the goal is to predict a continuous target variable.
• It is built using a recursive partitioning algorithm that splits the data into homogeneous subsets based on the values of the predictor variables.
• The algorithm finds the best split at each node using a criterion such as the sum of squared errors, variance reduction, or the coefficient of determination (R-squared).
• The result is a tree where each leaf node represents a prediction of the target variable based on the values of the predictor variables in that subset.
• The model is easy to interpret, and it can capture nonlinear relationships between the predictor variables and the target variable.
• However, it can suffer from overfitting, where the model fits the training data too closely and performs poorly on new, unseen data. Regularization techniques such as pruning or setting a minimum number of samples per leaf can help prevent overfitting.