This visualization demonstrates how Decision Trees work for time series forecasting using the classic airline passenger dataset. The model uses lagged features (Lag_1 and Lag_2) to predict future passenger counts.
Key Concept: Decision Trees partition the feature space using recursive splits. Each split creates a boundary that divides the data into regions with similar target values. The final prediction in each region is the average of all training points in that region.