Feature Engineering

Feature engineering is the art and science of creating input representations that help models learn effectively. Raw data is often not in a form that models can directly use: categorical variables like zip codes need encoding, numerical features may benefit from log transforms to capture diminishing returns, and interaction terms (e.g., bedrooms times square footage) can reveal relationships invisible to simple models.

In classical machine learning, feature engineering is where most of the intelligence resides. A well-engineered feature set with a simple linear model often outperforms a complex model on raw features, especially with limited data. Domain knowledge is critical: knowing that house prices have diminishing returns on size suggests using log(sqft), while knowing that neighborhood matters suggests one-hot encoding zip codes rather than treating them as numbers.

Deep learning automates much of feature engineering by learning representations directly from raw data -- early layers learn simple features (edges, textures), while deeper layers compose them into complex concepts. However, feature engineering remains important even in deep learning: choosing what data to include, how to preprocess it, and what domain-specific transformations to apply can significantly impact performance, especially with limited training data.