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Feature Engineering
Data / Training / Evaluation
Transforming raw data into informative model inputs.
Feature engineering is the process of selecting, transforming, or combining raw data into meaningful input features that improve model performance.
- Goal: Provide models with relevant, interpretable, and discriminative features.
- Examples: Normalization, one-hot encoding, PCA, derived metrics.