In a dataset with fluctuating values, you've applied Polynomial Regression, and the model seems to fit even the noise. What are the potential risks, and how could you mitigate them?
- Add more noise
- Ignore the noise
- Reduce model complexity through lower degree or regularization
- Use a linear model
The risk is overfitting the noise, which will harm the model's generalization ability. Reducing the polynomial degree or using regularization techniques can mitigate this by constraining the model's complexity.
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