Consider the following dataset. Each instance is annotated with information that indicates whether or not a professor’s course is worth taking given a description of the professor’s personality, the...

Consider the following dataset. Each instance is annotated with information that indicates whether or not a professor’s course is worth taking given a description of the professor’s personality, the difficulty of the course, the associated reviews on RateMyProfessors, and how easy it is to get an A in the course. A “+” indicates it is worth taking; a “-” indicates 1 it is not. Using these descriptions as training instances, show the decision tree that the ID3 decision tree learning algorithm would create to predict whether a particular professor’s course is worth taking. Show the information gain calculations that you computed to create the tree. Be sure to indicate the class value to associate with each leaf of the tree and the set of instances that are associated with each leaf.