Math & Optimization · intermediate · concept 43 of 176

📐Feature Engineering

The art of creating, transforming, and selecting input features to improve model performance. Often more impactful than model choice, 'garbage in, garbage out' applies doubly to ML. Feature selection is its own toolkit: filter methods score features statistically, wrappers like RFE search subsets, and embedded methods like L1 select while training. Imputation, filling missing values sensibly, is the unglamorous step most real datasets need first.