The correct answer is D. Regularized regression can help with bias trade-off, variance trade-off, and model selection.
Bias is the difference between the expected value of the model’s predictions and the true value of the target variable. Variance is the spread of the model’s predictions around the expected value. Regularized regression can help to reduce bias by adding a penalty term to the loss function that discourages the model from making large predictions. This can help to ensure that the model’s predictions are closer to the true value of the target variable. Regularized regression can also help to reduce variance by shrinking the model’s coefficients. This can help to prevent the model from overfitting the training data and making poor predictions on new data.
Model selection is the process of choosing the best model from a set of candidate models. Regularized regression can be used to help with model selection by providing a way to penalize models that are too complex. This can help to ensure that the model that is chosen is not overfit to the training data and will generalize well to new data.
In conclusion, regularized regression can help with bias trade-off, variance trade-off, and model selection. This makes it a powerful tool for machine learning.
Here are some additional details about each of the options:
- Option A: Regularized regression can help with bias trade-off. This is because the penalty term in the loss function discourages the model from making large predictions. This can help to ensure that the model’s predictions are closer to the true value of the target variable.
- Option B: Regularized regression can help with variance trade-off. This is because the penalty term in the loss function shrinks the model’s coefficients. This can help to prevent the model from overfitting the training data and making poor predictions on new data.
- Option C: Regularized regression can help with model selection. This is because the penalty term in the loss function can be used to penalize models that are too complex. This can help to ensure that the model that is chosen is not overfit to the training data and will generalize well to new data.