What are features and labels in Machine Learning?

Features are input variables used to make predictions, while labels are the target outputs that models learn to predict during training.\n\n• Features: Independent variables, predictors, input attributes\n• Labels: Dependent variables, targets, outputs to predict\n• Feature engineering: Creating meaningful features from raw data\n• Label quality: Accurate labels are essential for supervised learning\n\nExample: In email classification, features include sender domain, subject keywords, email length, attachment presence. The label is "spam" or "not spam." Good features help models distinguish between classes effectively.