ake no assumptions about the data; they split the data into smaller and smaller subgrioups of `\(x\)` covariates in terms of `\(y\)` and split on each predictor. How does it work? 

A decision tree predicts that an observation belongs to its correct class. 

For basic classification, we are no longer using MSE or RMSE but instead classification error rate. 

To determine the top split ("root node"), the model looks at all cutpoints to see which group stands out as the best split. The "complexity parameter" 