Decision Tree Splitting Criterion
(Redirected from splitting criterion)
- See: Splitting Criterion, Impurity Function.
- Algorithms for constructing decision trees usually work top-down, by choosing a variable at each step that best splits the set of items. Different algorithms use different metrics for measuring "best". These generally measure the homogeneity of the target variable within the subsets. Some examples are given below. These metrics are applied to each candidate subset, and the resulting values are combined (e.g., averaged) to provide a measure of the quality of the split.
- (Shih, 1999) ⇒ Yu-Shan Shih. (1999). “Families of splitting criteria for classification trees].” In: Statistics and Computing, 9(4). doi:10.1023/A:1008920224518.