What Tree-Based Models Are For

This lesson introduces what tree-based models are designed to do in practical machine learning workflows. It focuses on the core idea: using a sequence of simple questions to divide data into groups that are easier to predict.

Learners will see why decision trees are useful for classification and regression, why they are often easy to explain, and why random forests are commonly used when a single tree is too unstable. Later lessons will handle split criteria, pruning, overfitting, feature importance, and model evaluation in more detail.