Classification

Overview Classification and regression are the two core types of supervised learning, distinguished by what kind of output they predict. Classification assigns inputs to a discrete class, while regression estimates a continuous value. Picking the wrong one for your target variable leads to mismatched loss functions, evaluation metrics, and model outputs. Comparison Diagram ClassificationRegressionClass AClass Boutput: discrete categoryoutput: continuous number Comparison Table Aspect Classification Regression Target variable type Discrete, categorical labels from a finite set of classes Continuous, ordered numeric values Learning objective Learn a decision boundary that separates classes Learn a function mapping inputs to a continuous output Typical loss function Cross-entropy, log loss, or hinge loss Mean squared error or mean absolute error Model output format Class label or probability distribution over classes Single scalar value (or vector of scalars) Common algorithms Logistic regression, SVM, decision trees, kNN, softmax networks Linear regression, ridge/lasso, decision trees, kNN, regression networks Evaluation metrics Accuracy, precision/recall, F1, ROC-AUC, confusion matrix RMSE, MAE, R-squared, MAPE Error interpretation Prediction is simply right, wrong, or confused with another class Prediction error has magnitude and direction, showing how far off it was Key Differences Classification predicts a discrete label from a fixed set of classes, while regression predicts a continuous value on a numeric scale. Classification models typically optimize cross-entropy loss to separate classes, while regression models optimize squared error to minimize distance from the true value. Classification is evaluated with metrics like accuracy/F1, while regression is evaluated with metrics like RMSE/R-squared. A classification error is simply right, wrong, or a class confusion, while a regression error carries a magnitude showing how far off the prediction was. When to Use Each Classification ...