Employ a stepwise feature selection method, iteratively adding or removing features based on their statistical significance to refine the model.

Apply principal component analysis (PCA) to transform the features into a lower-dimensional space, thereby indirectly removing non-informative features.

Implement L1 regularization (Lasso) to penalize the absolute size of the coefficients, effectively shrinking the coefficients of non-informative features to zero.

Use Shapley values post-model construction to identify and manually remove features that contribute the least to the model's predictions.

Combine both L1 regularization for initial feature selection and Shapley values for a secondary validation of feature importance. Choose two correct options.