Question 72
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.
References
https://developers.google.com/machine-learning/glossary#L1_regularization
https://www.quora.com/How-does-the-L1-regularization-method-help-in-feature-selection