How to select Best Split in Decision Trees using Chi-Square
Note: If you are more interested in learning concepts in an Audio-Visual format, We have this entire article explained in the video below. If not, you may continue reading.
What is Chi-Square?
Higher the chi-square value more will be the purity of the nodes after a split.
Properties of chi-square
- Chi-square just like Gini impurity works only with categorical variables so we cannot use it for continuous targets.
- The higher the value of chi-square more the sub-nodes are different from the parent node and hence the homogeneity is more.
Steps to Calculate Chi-Square for a split-
- First, we need to calculate the expected values for each class.
- Then we calculate the chi-square for individual nodes using this formula that we’ve seen before-
- Finally, we calculate the chi-square for split using the sum of the chi-square of each child node for that split.
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