6.2 CLASSIFICATION AND REGRESSION TREES
The CART method3produces decision trees that are strictly binary, containing exactly two branches for each decision node. CART recursively partitions the records in the training data set into subsets of records with similar values for the target attribute. The CART algorithm grows the tree by conducting for each decision node, an exhaustive search of all available variables and all possible splitting values, selecting the optimal split according to the Gini Index (from Kennedy et al.4).
Let Φ(s | t) be a measure of the “goodness” of a candidate split s at node t, where
![equation](https://static.packt-cdn.com/products/9781119526810/graphics/images/c6-disp-0001.png)
and where
![equation](https://static.packt-cdn.com/products/9781119526810/graphics/images/c6-disp-0002.png)