- Year not given
Feature selection vs feature extraction
Short answer
Feature selection chooses a subset of original attributes, preserving meaning. Feature extraction creates new attributes by transforming originals, often losing interpretability. Selection is used when interpretability matters; extraction when many correlated features exist.
Differentiate between feature selection and feature extraction.
Feature selection chooses a subset of the existing attributes and discards the rest. Feature extraction builds a smaller set of new attributes by transforming or combining all the original ones.
| Basis | Feature selection | Feature extraction |
|---|---|---|
| Idea | Select the best subset of original features | Create new features from the originals |
| Features after | Same features, fewer in number | New, transformed features |
| Meaning | Original meaning kept; easy to interpret | Meaning often lost (e.g. "PC1") |
| Examples | Information gain, chi-square, forward/backward selection | PCA, SVD, LDA, wavelets, autoencoders |
| Information | Dropped features' information is lost | Information from all features is compressed |
| Cost | Subset search can be costly (wrappers) | Matrix computations (eigenvectors) can be costly |
| Need to measure the dropped features? | No, only the selected ones are needed later | Yes, all original features are needed to compute the new ones |
| Typical use | When interpretability and cost of measuring matter | When many correlated features exist; visualisation |
Example: from attributes {height, weight, BMI, name}, selection may keep {BMI}; extraction (PCA) would combine height and weight into one component.
✓ Answer checked by Claude Opus 5.5 (high effort)
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