What is the difference between regression and classification models?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in how they process and learn from data.

The main difference between regression and classification models lies in the type of output they predict:

• Regression models predict continuous numeric values.

  • Example: Predicting house prices, temperature, or stock prices.

  • Output: A real number like 250,000 or 72.5.

• Classification models predict discrete labels or categories.

• Example: Determining if an email is spam or not, or recognizing handwritten digits.

• Output: A class like "spam" or "not spam", or digits 0–9. 

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