Assume that the following training set and the test set are given for carBought function. Training Set Test Set income seX married carBought married carBought income sex low middle male yes middle female no low low middle male yes middle female yes male no es female no middle male yes middle male no male female no no middle female yes male yes es female yes middle male yes es high low low es male male high female no low middle male high female yes yes middle male no female yes es middlefemale no male yes high high male no no highfemale yes male yes yes low middle male yes es middle female yes highmale yes es male no female no high a) Create a decision tree using the training set. Use information gain as attribute selection measure. Show details of your creation (gain and information values) b) Compute classifier evaluation metrics (Precision and Recall, and F-measure) using the test set c) Create a decision tree using the training set using Gini index. Show details of your creation (gain values) Assume that the following training set and the test set are given for carBought function. Training Set Test Set income seX married carBought married carBought income sex low middle male yes middle female no low low middle male yes middle female yes male no es female no middle male yes middle male no male female no no middle female yes male yes es female yes middle male yes es high low low es male male high female no low middle male high female yes yes middle male no female yes es middlefemale no male yes high high male no no highfemale yes male yes yes low middle male yes es middle female yes highmale yes es male no female no high a) Create a decision tree using the training set. Use information gain as attribute selection measure. Show details of your creation (gain and information values) b) Compute classifier evaluation metrics (Precision and Recall, and F-measure) using the test set c) Create a decision tree using the training set using Gini index. Show details of your creation (gain values)