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Machine Learning FAQ

E.g., “keep all features that have a variance greater or equal to x” or “keep the the top k features with the largest variance.” We assume that features with a higher variance may contain more useful information, but note that we are not taking the relationship between feature variables or feature and target variables into account, which is one of the drawbacks of filter methods.

A Wrapper Method Example: Sequential Feature Selection Sequential Forward Selection (SFS), a special case of sequential feature selection, is a greedy search algorithm that attempts to find the “optimal” feature subset by iteratively selecting features based on the classifier performance.

Through adding the L1 term, our objective function now becomes the minimization of the regularized cost, and since the penalty term grows with the value of the weight parameters (λ is just a free parameter to fine-tune the regularization strength), we can induce sparsity through this L1 vector norm, which can be considered as an intrinsic way of feature selection that is part of the model training step.

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