5. Bias vs Variance (20 points): (a) (5 points) State briefly what you understand by the bias-variance tradeoff. (b) (5 points) What happens to the bias and variance when the number of training samples increases? (c) (10 points) Suppose you decide to use the k-Nearest Neighbors (KNN) classifier for a classification problem. In this problem, assume that you are given a fixed number of training samples. You can choose either 1 or 100 as your value of k, the number of neighbors to be considered for classification. Which k would give you higher variance? Which k would give you higher bias? Explain