Explain the difference between classification and regression.

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Classification and regression are two primary types of supervised machine learning tasks, differing in the nature of their output.

Classification:

  • Predicts categorical labels or classes.

  • The goal is to assign input data to one of several discrete categories.

  • Examples include:

    • Email spam detection (spam or not spam)

    • Diagnosing diseases (disease A, B, or none)

    • Image recognition (cat, dog, car)

  • Output is typically a class label or a probability distribution over classes.

Regression:

  • Predicts continuous numerical values.

  • The goal is to estimate a quantity based on input features.

  • Examples include:

    • Predicting house prices based on size and location.

    • Forecasting temperature or stock prices.

    • Estimating a patient’s blood pressure level.

  • Output is a real-valued number.

In summary, classification sorts data into categories, while regression predicts a continuous outcome. Both are foundational in machine learning and used depending on the problem type.

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