What is the difference between classification and regression problems?

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Classification and regression are two main types of supervised machine learning problems, and they differ based on the type of output they predict.

Classification:

• Goal: Predict a category or class label.

• Output: Discrete values (e.g., "spam" or "not spam", "yes" or "no", or multiple categories like "cat", "dog", "bird").

• Examples: Email spam detection, disease diagnosis (sick or healthy), image recognition.

• Algorithms used: Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), Neural Networks for classification.

Regression:

• Goal: Predict a continuous numeric value.

• Output: Real numbers (e.g., temperature, price, age).

• Examples: Predicting house prices, stock market trends, or sales forecasting.

• Algorithms used: Linear Regression, Ridge/Lasso Regression, Decision Trees, Random Forest, Gradient Boosting, Neural Networks for regression.

Key Difference:

• Classification assigns inputs to groups or labels.

• Regression estimates a numeric value based on input features.

In short, if the answer you're trying to predict is a number, it's a regression problem; if it's a category or label, it's a classification problem.

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