Desktop Survival Guide
by Graham Williams


As we have seen intuitively, the algorithm for building decision trees is quite simple. The complexities come from the choice of criteria in choosing a variable at each node, and then in pruning the decision tree.

In searching for a decision tree to best model our data, alternative decision trees are considered in a top-down fashion, beginning with the decision of the variable to initially partition the data (at the root node).

Todo: Measures include deciding which variable to split the tree on, and deciding when to stop. and deciding how to prune through the cp, etc.

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