What happens to a k-NN model as you increase the value of k? (Select One correct answer)A. The variance increasesB. The bias increases.
Question
What happens to a k-NN model as you increase the value of k? (Select One correct answer)A. The variance increasesB. The bias increases.
Solution
To answer the question, let's break down the concepts of variance and bias in the context of a k-NN (k-Nearest Neighbors) model:
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Variance: This refers to the model's sensitivity to fluctuations in the training data. A model with high variance pays a lot of attention to the training data, which can lead to overfitting.
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Bias: This refers to the error introduced by approximating a real-world problem, which may be complex, by a simplified model. A model with high bias makes strong assumptions about the data, which can lead to underfitting.
Now, let's consider what happens when we increase the value of k in a k-NN model:
- When k is small (e.g., k=1), the model is very flexible and can fit the training data very closely, which means it has low bias but high variance.
- As we increase k, the model starts to consider more neighbors when making predictions. This makes the decision boundary smoother and less sensitive to the noise in the training data, which reduces variance.
- However, as k becomes very large, the model starts to average over a larger number of neighbors, which can lead to a loss of detail and an increase in bias.
Therefore, increasing the value of k in a k-NN model generally leads to an increase in bias and a decrease in variance.
The correct answer is: B. The bias increases.
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