Supervised Learning

Supervised learning is a type of machine learning where an algorithm is trained on labeled data. The goal is to learn a mapping from inputs to outputs, allowing the model to predict outcomes for new, unseen data.

Key Concepts

• Labelled Data: Each training example includes an input vector and a target output.
• Classification: Predict categorical labels (e.g., spam vs. not‑spam).
• Regression: Predict continuous values (e.g., house prices).
• Loss Function: Measures the discrepancy between predicted and true values.
• Model Evaluation: Metrics such as accuracy, precision, recall, RMSE, etc.

Popular Algorithms

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forests
• Support Vector Machines
• k‑Nearest Neighbors
• Neural Networks