What is the difference between classification and regression ?

 

1. Classification

Definition: Classification is about predicting categorical labels (discrete classes).

• Goal: Assign input data to one of several predefined categories.
• Output: A class label (e.g., "Yes" or "No", "Dog" or "Cat").
• Examples:
  • Email spam detection (Spam / Not Spam)
  • Disease diagnosis (Positive / Negative)
  • Image recognition (Cat / Dog / Bird)

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2. Regression

Definition: Regression is about predicting continuous numeric values.

• Goal: Estimate a real-valued output based on input features.
• Output: A number (e.g., price, temperature, age).
• Examples:
  • Predicting house prices
  • Forecasting stock prices
  • Estimating temperature for tomorrow

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Key Differences

Aspect	Classification	Regression
Output Type	Discrete (categories)	Continuous (numeric)
Examples	Spam detection, sentiment analysis	Price prediction, demand forecasting
Evaluation Metrics	Accuracy, F1-score, ROC-AUC	RMSE, MAE, R²