State briefly what you understand by the bias-variance tradeoff. (b) (5 points) What happens to the...

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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. 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.

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a The biasvariance tradeoff is a fundamental concept in machine learning that refers to the balance ... View the full answer