CIS_5200_Fall_2023_Final_Practice_Problems

3 SAMPLE SHORT ANSWER QUESTIONS (a) [2 points] Suppose you are given the following dataset, along with an ensemble H ( x ) that makes the following predictions: x y H ( x ) 1 5 5.5 2 6 5.5 3 7 7.5 4 8 7.5 You are training a gradient boosted regression tree, boosting the squared loss. What dataset would you train your next weak learner on? List both the feature values and labels you would call the weak learner algorithm with. (b) [2 points] Suppose you determine that 0 . 99 n of your n training labels are +1. Would you expect accuracy to be a good measure of performance in this scenario? If yes, why? If not, what are some alternative performance metrics that you might use instead? (c) [2 points] Suppose you run PCA on a training dataset { x 1 , ..., x n } . After computing all eigen- values of the sample covariance matrix λ 1 , ..., λ n , you observe that: λ 1 + λ 2 λ 1 + · · · + λ n = 0 . 98 What does this mean about your data? (d) [2 points] Suppose you are training a decision tree on a binary classification dataset with 10 traning examples. You are considering a feature split that splits your dataset into the following two subsets: (a) Subset 1: 6 instances (4 positive labels, 2 negative labels) (b) Subset 2: 4 instances (1 positive label, 3 negative labels) Name : Penn ID :