10. What are the differences between classification and regression in machine learning?

Classification and regression are two fundamental types of problems in machine learning, differing in the type of values they predict.

Classification:

• The goal is to assign samples to one of several discrete categories.
• The result is a class label (category).
• Example applications: image recognition, medical diagnosis, email spam detection.
• Example algorithms: Decision Trees, SVM, Naive Bayes, KNN.

Regression:

• The goal is to predict a continuous numerical value.
• The result is a real number.
• Example applications: price forecasting, trend analysis, sales prediction.
• Example algorithms: Linear Regression, Polynomial Regression, Regression Trees.

Main differences:

1. Type of result: Classification predicts categories, while regression predicts numerical values.
2. Evaluation metrics: Classification uses metrics like accuracy, precision, recall, while regression uses RMSE, MAE, R².
3. Applications: Classification is used for categorical decision-making tasks, while regression is used for quantitative prediction tasks.

Classification and regression are the foundation of many machine learning applications, enabling the solution of a wide range of problems.