The correct answer is: D. ridge has smaller bias, smaller variance.
Ridge regression is a regularization method that penalizes the model for having large coefficients. This can help to prevent overfitting, which can occur when the model learns the noise in the data instead of the underlying signal.
The bias-variance trade-off is a fundamental concept in machine learning. It refers to the fact that any model will have some amount of bias and variance. Bias is the difference between the model’s predictions and the true values. Variance is the variability of the model’s predictions.
In general, a model with low bias will have high variance, and a model with low variance will have high bias. The goal of machine learning is to find a model that minimizes both bias and variance.
Ridge regression can help to reduce bias by shrinking the coefficients of the model. This can help to prevent the model from overfitting the data. However, ridge regression can also increase variance. This is because ridge regression penalizes the model for having large coefficients, which can lead to the model being less flexible.
The optimal amount of regularization to use depends on the specific data set. In general, it is important to use a regularization parameter that is large enough to reduce bias, but not so large that it increases variance.
Here is a more detailed explanation of each option:
- Option A: ridge has larger bias, larger variance. This is not the correct answer because ridge regression can help to reduce bias.
- Option B: ridge has smaller bias, larger variance. This is not the correct answer because ridge regression can increase variance.
- Option C: ridge has larger bias, smaller variance. This is not the correct answer because ridge regression can help to reduce bias.
- Option D: ridge has smaller bias, smaller variance. This is the correct answer because ridge regression can help to reduce both bias and variance.