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Feature Engineering

The process of transforming raw data into more informative inputs (features) for ML models.

Goes beyond encoding: you can create new features/metrics (like derived columns in the DB world) that pure encoding does not offer.

The goal of FE is to improve model accuracy, interpretability, and generalization.

Example (Laptop Sales):

Purchase Date = 2025-09-02

Derived Features:

• Month = 09
• DayOfWeek = Tuesday
• IsHolidaySeason = No
• IsWeekend = No
• IsLeapYear= No
• Quarter = Q3

Encoding (One-Hot, Label, Target) = only turns categories into numbers.

But real-world data often hides useful patterns in dates, interactions, domain knowledge, or semantics.

• Encoding only handles Product → One-Hot or Target.

Feature Engineering adds new insights:

• From Purchase Date: extract Month, DayOfWeek, IsHolidaySeason.
• From Price: create Discounted? (if < avg product price).
• Combine features: Price / AvgCategoryPrice.

Basic Feature Engineering

Improve signals/patterns without domain-specific knowledge.

Scaling/Normalization: Price → (Price – mean) / std

Date/Time Features: Purchase Date → Month=12, DayOfWeek=Friday

Polynomial/Interaction: Price × Tier

Pros:

• Easy to implement.
• Immediately boosts many models (especially linear/Neural Networks).

Cons:

• Risk of adding noise if done blindly.
• Limited unless combined with domain insights.

Domain-Specific Feature Engineering

Apply business/field knowledge.

Examples:

Finance: Debt-to-Income Ratio, Credit Utilization %

Healthcare: BMI = Weight / Height², risk score categories

IoT: Rolling averages, peak detection in sensor data.

Pros:

• Captures real-world meaning → big performance gains.
• Makes models explainable to stakeholders.

Cons:

• Requires domain expertise.
• Not always transferable between datasets.

#feature_engineering #domain_specificVer 0.3.6