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How do changes in model features and regularization options illustrate the bias-variance tradeoff?
Question: Explain the concept of the bias-variance tradeoff as described in Machine Learning Yearning. In your explanation, detail how changing a model's features versus adding regularization influences both bias and variance errors.
Sample answer: The bias-variance tradeoff is a situation where modifications to a learning algorithm that reduce bias error typically increase variance error, and vice versa. For instance, adding input features to a model generally reduces bias but can increase variance. In contrast, adding regularization generally reduces variance but at the cost of increasing bias.
Key points:
- The bias-variance tradeoff refers to changes in a learning algorithm that reduce one source of error (bias or variance) at the expense of increasing the other.
- Adding input features generally reduces bias but can increase variance.
- Adding regularization to a model generally increases bias but reduces variance.
Rubric: The response should correctly define the bias-variance tradeoff as a balance where reducing one type of error increases the other. It must specify that adding features reduces bias but increases variance, and adding regularization increases bias but reduces variance.
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