In classification, a decision tree is constructed by recursive binary splitting and growing each node into left and right children. In each partition, it greedily searches for the most significant combination of feature and its value as the optimal splitting point. The quality of separation is measured by the weighted purity of labels of two resulting children, specifically via metric Gini Impurity or Information Gain. In regression, the tree construction process is almost identical to the classification one, with only two differences due to the fact that the target becomes continuous:
- The quality of splitting point is now measured by the weighted mean squared error (MSE) of two ...
