Q3. (10 points) Consider the following setting. You are provided with n training examples: (₁, ₁), (2, 2), (In, Yn), where zi is the input example, and y, is the class

label (+1 or -1). However, the training data is highly imbalanced (say 90% of the examples are negative and 10% of the examples are positive) and we care more about the accuracy of positive examples. How will you modify the perceptron algorithm to solve this learning problem? Please justify your answer.