Decision Tree Regressor, Explained: A Visual Guide with Code Examples
REGRESSION ALGORITHM
Decision Tree Classifier, Explained: A Visual Guide with Code Examples for Beginners
Decision Trees aren't limited to categorizing data – they're equally good at predicting numerical values! Classification trees often steal the spotlight, but Decision Tree Regressors (or Regression Trees) are powerful and versatile tools in the world of continuous variable prediction.
While we'll discuss the mechanics of regression tree construction (which are mostly similar to the classification tree), here, we'll also advance beyond the pre-pruning methods like "minimal sample leaf" and "max tree depth" introduced in the classifier article. We'll explore the most common post-pruning method which is cost complexity pruning, that introduces a complexity parameter to the decision tree's cost function.
Definition
A Decision Tree for regression is a model that predicts numerical values using a tree-like structure. It splits data based on key features, starting from a root question and branching out. Each node asks about a feature, dividing data further until reaching leaf nodes with final predictions. To get a result, you follow the path matching your data's features from root to leaf.
