Cost complexity pruning

For each value of $\alpha$ there corresponds a subtree $T \in T_0$ such that $$\sum_{m=1}^{\overline{T}}\sum_{i:x_i\in R_m}(y_i-\overline{y}_{R_m})^2+\alpha\overline{T},$$ is as small as possible. Here $\overline{T}$ is the number of terminal nodes of the tree $T$ , $R_m$ is the rectangle (i.e. the subset of predictor space) corresponding to the $m$-th terminal node.

The tuning parameter $\alpha$ controls a trade-off between the subtree’s com- plexity and its fit to the training data. When $\alpha = 0$, then the subtree $T$ will simply equal $T_0$, because then the above equation just measures the training error. However, as $\alpha$ increases, there is a price to pay for having a tree with many terminal nodes. The above equation will tend to be minimized for a smaller subtree.

It turns out that as we increase $\alpha$ from zero branches get pruned from the tree in a nested and predictable fashion, so obtaining the whole sequence of subtrees as a function of $\alpha$ is easy. We can select a value of $\alpha$ using a validation set or using cross-validation. We then return to the full data set and obtain the subtree corresponding to $\alpha$.

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