Which technique should you use?

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 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.

Answer: B

Explanation:

https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/overview#sampled-shapley