8.7 Recursive binary splitting (continued)

1. We first select the predictor \(X_j\) and the cutpoint \(s\) such that splitting the predictor space into the regions \({\{X|X_j<s\}}\) and \({\{X|X_j{\ge}s}\}\) leads to the greatest possible reduction in RSS

2. Repeat the process looking for the best predictor and best cutpoint to split data further (i.e., split one of the 2 previously identified regions - not the entire predictor space) minimizing the RSS within each of the resulting regions

3. Continue until a stopping criterion is reached, e.g., no region contains more than five observations

4. Again, we predict the response for a given test observation using the mean of the training observations in the region to which that test observation belongs