Classification

Classification is a fundamental task in machine learning where we train models to categorize data into predefined classes or categories. Algorithms learn patterns from labeled examples to make predictions on new, unseen data.

Example: Classification is like sorting emails into folders such as "important," "promotions," or "spam." Decisions are based on features like sender, subject, and content. Problems include binary, multi-class, and multi-label classification. Various algorithms tackle classification differently, using techniques like logistic regression, SVMs, decision trees, and neural networks. Models are evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC curve area. Real-world applications include email filtering, sentiment analysis, medical diagnosis, face recognition, and fraud detection.