Question 1

What's the difference between feature engineering and feature selection?

Accepted Answer

Feature engineering creates or transforms inputs (e.g., turning timestamps into “day of week” or combining fields into “total rooms”). Feature selection chooses which existing features to keep (e.g., dropping redundant or noisy columns) to improve accuracy, speed, or interpretability.

Question 2

When should I use feature engineering?

Accepted Answer

Use it when raw data isn’t directly useful to a model or when you need to capture domain patterns (seasonality, ratios, interactions, text signals). It’s especially helpful for tabular ML (fraud, churn, pricing, forecasting) and when you need explainable, stable inputs. For deep learning on images/audio, feature engineering is often lighter, but you still engineer labels, augmentations, and metadata features.

Question 3

How much does feature engineering cost?

Accepted Answer

There’s no fixed price—cost depends on (1) compute for transformations (CPU/GPU hours), (2) storage for intermediate datasets and feature tables, (3) data processing services (ETL/ELT jobs, streaming), and (4) operational overhead (orchestration, monitoring, data quality). Costs rise with data volume, refresh frequency (batch vs near-real-time), and complexity (joins, aggregations, window functions). A common approach is to start with batch features on a schedule, then move only high-value features to real-time serving.

Feature Engineering

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