In which situation will ridge regression outperform lasso regression in terms of prediction accuracy?

Ridge regression is particularly advantageous when the response variable is influenced by all of the predictors in the model, especially when there are many predictors with potential multicollinearity. This means that ridge regression is designed to handle situations where all predictors contribute to the response, unlike lasso regression, which might shrink some coefficients to zero and thereby exclude certain predictors from the model.

In scenarios where the true relationship is complex and all predictors have some degree of contribution, ridge regression maintains all predictors in the model, leading to more robust estimates. This helps in improving prediction accuracy as it mitigates the risk of underfitting, which can occur if some predictors are disregarded entirely, as in the case of lasso.

Moreover, ridge regression applies a penalty to the size of coefficients rather than eliminating them, allowing it to capture more of the variability explained by the predictors. Therefore, when all predictors are relevant to the response, ridge regression can take advantage of this comprehensive input, leading to better predictive performance compared to lasso regression, which may introduce excessive bias by excluding potentially useful variables.