What should the ML specialist do to improve the model results?

Explanations:

Increasing the L1 regularization parameter would further penalize the coefficients, likely resulting in even more features being set to zero. This would not solve the overfitting issue and would lead to a model with no features.

Decreasing the L1 regularization parameter would reduce the penalty on the coefficients, allowing some features to retain non-zero weights. This could help balance the trade-off between fitting the training data and generalizing to unseen data, thus improving model performance.

Introducing a large L2 regularization parameter while keeping the current L1 value could lead to a more complex model that might still not generalize well. This may also conflict with the sparsity encouraged by L1 regularization, potentially complicating the model without addressing the overfitting issue effectively.

Introducing a small L2 regularization parameter without changing the current L1 regularization value might not be sufficient to counteract the extreme sparsity induced by L1 regularization. This could still leave the model with many zero weights, failing to improve performance effectively.