Feature Engineering

Category: science

The process of using domain knowledge to select and transform raw data into a format that ML models can learn from.

Features are the "ingredients." Raw data (a timestamp) might be useless, but the "Feature" (Day of Week) is highly predictive. Feature engineering is where the human expert outsmarts the algorithm by highlighting the patterns that matter most.

Common Examples

• We spent the week on feature engineering, converting raw transaction logs into "velocity of spend" ratios that improved model performance.
• The most skilled data scientists spend 80% of their time on feature engineering, as it is the true driver of high-performing predictive models.