| Aspect | Classification Method | Regression Method |
|---|---|---|
| Task | Predicts the class label or category of a data instance | Predicts continuous numerical values for a given input |
| Output Type | Discrete (categorical classes or labels) | Continuous (real-valued numbers) |
| Examples | Email spam detection, image classification, sentiment analysis | House price prediction, stock market forecasting, age estimation |
| Evaluation Metrics | Accuracy, precision, recall, F1-score, ROC-AUC | Mean squared error (MSE), R-squared (R2), mean absolute error (MAE) |
| Model Interpretation | Provides insights into the decision boundaries between classes | Allows understanding of how each input feature impacts the output |
| Common Algorithms | Decision Trees, Random Forest, Support Vector Machines (SVM), Neural Networks | Linear Regression, Polynomial Regression, Gradient Boosting |
| Use Cases | Medical diagnosis, sentiment analysis, fraud detection | Predicting sales, temperature forecasting, demand prediction |
| Loss Function | Typically uses cross-entropy or log loss for optimization | Typically uses Mean Squared Error (MSE) for optimization |
| Example Formula | Class = f(input_features) | Output = f(input_features) |
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