기능 엔지니어링

In classic machine learning it is often the single biggest driver of accuracy, more than the choice of algorithm.

A model can only learn from the inputs you give it, and raw data rarely arrives in a useful form. Feature engineering reshapes it: extracting the day-of-week from a timestamp, computing a customer's average purchase, encoding categories as numbers, scaling values to a common range, or combining columns into ratios. Done well, it exposes the patterns an algorithm needs, so a simple model on great features often beats a complex model on raw data. It also requires domain knowledge, since knowing that, say, 'transactions per minute' signals fraud is what creates a powerful feature. The classic risk is data leakage, accidentally building a feature from information that wouldn't be available at prediction time, which inflates test scores but fails in production. 딥 러닝은 이 중 일부를 자동화하지만 구조적/표 형식 문제는 여전히 여기에 크게 의존합니다.