What is the difference between regression and classification?

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🔑 Regression

• Definition: Regression predicts a continuous numeric value.

• Output: A real number (e.g., 3.5, 100.75).

• Examples:

  • Predicting house prices.

  • Estimating a person’s weight based on height.

  • Forecasting stock prices.

• Evaluation Metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R² score.

🔑 Classification

• Definition: Classification predicts a discrete label or category.

• Output: A class label (e.g., “spam” or “not spam”).

• Examples:

  • Email spam detection.

  • Diagnosing a disease (positive/negative).

  • Image recognition (cat, dog, bird).

• Evaluation Metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC.

📊 Key Differences

Aspect	Regression	Classification
Output Type	Continuous numeric value	Discrete label or category
Examples	Price prediction, temperature	Spam detection, sentiment analysis
Algorithms	Linear Regression, Ridge, Lasso	Logistic Regression, Decision Trees, SVM
Metrics	MSE, RMSE, MAE, R²	Accuracy, Precision, Recall, F1

✅ In short:

• Regression = predicting numbers.

• Classification = predicting categories.

Read More :

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What is hypothesis testing?

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