Decision Tree vs Linear Regression
The data follows a curved relationship: y = x/2 + sin(x) + noise. Watch how a straight line and a decision tree try to learn the same nonlinear pattern.
Linear Regression
Creates one straight line. It captures the overall direction, but it cannot follow every curve.
Decision Tree Regression
Divides the input into regions. Each region receives its own prediction, creating a staircase-shaped curve.