Which solution will meet these requirements?
Retrain the model with L1 regularization applied.
Retrain the model with L2 regularization applied.
Retrain the model with dropout regularization applied.
Retrain the model by using more data.
Explanations:
L1 regularization (Lasso) helps in feature selection by adding a penalty proportional to the absolute value of coefficients, effectively reducing the number of features by shrinking some coefficients to zero, preventing overfitting.
L2 regularization (Ridge) helps to reduce overfitting but does not perform feature selection. It penalizes large coefficients but does not shrink them to zero, so it does not reduce the number of features.
Dropout is a technique used in neural networks, not linear models. It randomly drops units during training to prevent overfitting. This is not applicable to a linear model.
More data can help reduce overfitting, but it does not directly address feature selection. The question specifically asks for a solution to reduce the number of features.
It seems to me that the answer is:
Retrain the model with L1 regularization applied.