Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and variance with lambda.

The correct answer is: C. In case of very large lambda; bias is high, variance is low.

Ridge regression is a regularization method that penalizes the model for having large coefficients. This can help to reduce the variance of the model, but it can also increase the bias. In the case of very large lambda, the model will be too heavily penalized and will have high bias and low variance.

Here is a more detailed explanation of each option:

• Option A: In case of very large lambda; bias is low, variance is low. This is not possible, as ridge regression always increases the bias.
• Option B: In case of very large lambda; bias is low, variance is high. This is possible, as ridge regression can reduce the variance of the model. However, it is important to note that the bias will also increase as lambda increases.
• Option C: In case of very large lambda; bias is high, variance is low. This is the correct answer, as it is the only option that is consistent with the properties of ridge regression.
• Option D: In case of very large lambda; bias is high, variance is high. This is not possible, as ridge regression always reduces the variance of the model.

I hope this explanation is helpful. Please let me know if you have any other questions.