Classification

Classification is a supervised machine learning technique: it predicts a discrete, categorical label for new data based on patterns learned from labeled training data.

Classification is a core supervised learning task where the model sorts input data into predefined classes (categories). The process requires a robust, labeled training dataset, which allows the algorithm to map input features to a specific output label (e.g., 'Spam' or 'Not Spam'). Key algorithms like Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN) execute this prediction. Real-world applications are critical: think financial models identifying a loan applicant as 'High Risk' or 'Low Risk,' or medical imaging systems classifying a tumor as 'Benign' or 'Malignant.' The goal is high accuracy across all classes, often measured by metrics like the F1 score and AUC (Area Under the Curve).

https://www.datacamp.com/tutorial/classification-machine-learning