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AI & Machine Learning · Foundational

Feature Engineering

Creating, transforming and selecting input variables to improve model performance.

Definition

Feature engineering applies domain knowledge to turn raw data into informative signals, such as ratios, date parts, aggregates, text counts or encodings. It also covers scaling, binning, handling missing values and encoding categoricals. In classical ML it often matters more than algorithm choice.

Example

Deriving days_since_last_purchase from a raw timestamp column.

Exam tip

Deep learning reduces but does not eliminate the need for feature engineering on tabular data.

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