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