4. [Decision Trees] (6 pts) In class, we covered how to learn binary decision trees. In this question, you will modify the decision tree learning algorithm to obtain ternary decision trees (i.e, each internal node has three children). Assume all features are real-valued, and the labels are categorical (i.e., classification). 2 a. (2 pts) Each internal node should split a single feature into three intervals x; t1, t > x; t2, and t > xj. Write down all parameters for this split. b. (2 pts) Write down the information gain for a single split. You do not have to expand the entropy (e.g., for traditional decision trees, the information gain is IG(j, t) = H (Z) - H(Z[x; t])P(x; t) - H(Z[x;