You are building a linear model with over 100 input features, all with values between –1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?

A. Use principal component analysis (PCA) to eliminate the least informative features. B. Use L1 regularization to reduce the coefficients of uninformative features to 0. C. After building your model, use Shapley values to determine which features are the most informative. D. Use an iterative dropout technique to identify which features do not degrade the model when removed.