Feature Engineering
The process of creating and selecting input variables (features) for machine learning models. Good features capture relevant patterns and improve model performance.
In-Depth Explanation
Feature engineering transforms raw data into features that better represent underlying patterns for ML models. It's often the difference between mediocre and excellent model performance.
Feature engineering techniques:
- Transformation: Log, sqrt, binning
- Encoding: One-hot, label, target encoding
- Aggregation: Sum, mean, count over groups
- Date/time: Day of week, month, holiday flags
- Text: TF-IDF, word counts, sentiment scores
- Interaction: Combining features mathematically
Feature selection:
- Remove redundant features
- Select most predictive features
- Reduce dimensionality
- Improve training speed
- Prevent overfitting
Modern approaches:
- Automated feature engineering (Featuretools)
- Deep learning learns features automatically
- But domain expertise still valuable
Business Context
Feature engineering is where domain knowledge meets data science. Business understanding of what drives outcomes translates to better features.
How Clever Ops Uses This
We combine domain expertise with data science to engineer features for Australian business ML projects, improving model accuracy significantly.
Example Use Case
"Creating features like "days since last purchase," "average order value," and "purchase frequency" from transaction data for customer churn prediction."