Data and feature engineering · MLD-0418
Feature engineering
Designing input variables or transformations using task knowledge.
Original reference page · detailed expansion pending. Original definitions, examples, notes and citations are preserved below. New examples are illustrative, not benchmark results.
Original example
Create time-since-last-purchase from event timestamps.
Original caveat
Engineered features can leak target information.
Context and aliases
Level: Intermediate. Stages: Data · Training · Evaluation.
Tags: ML · DL · SLM · LLM.
Aliases: None recorded.
Related concepts
Concepts linking here
Technology connections
- Apache Arrow — Columnar in-memory data format and interoperability libraries.
- CuPy — NumPy-like GPU array library.
- DuckDB — Embedded analytical SQL database.
- Numba — JIT compiler for supported Python and NumPy code.
- NumPy — N-dimensional arrays and vectorized numerical operations for Python.
- pandas — Labeled table and time-series manipulation in Python.
- Polars — Columnar DataFrame engine with eager and lazy APIs.
- SciPy — Scientific algorithms built on NumPy.
- SymPy — Python library for symbolic mathematics.
Original references
- scikit-learn: cross-validation ↗ · Reference link
- scikit-learn: missing-value imputation ↗ · Reference link
- scikit-learn: preprocessing ↗ · Reference link
- scikit-learn: feature selection ↗ · Reference link
- Original reference URL ↗