Supervised Learning

Supervised learning involves training models on labeled data, where the correct answers are provided. Classification predicts discrete categories (like spam/not spam), while regression predicts continuous values (like house prices). Key algorithms include: linear models (Linear Regression, Logistic Regression), tree-based models (Decision Trees, Random Forests, Gradient Boosting), support vector machines, and k-nearest neighbors. Each algorithm has strengths and weaknesses depending on the data characteristics. The supervised learning process involves: splitting data into training and test sets, training the model, making predictions, and evaluating performance using appropriate metrics (accuracy, precision, recall for classification; MSE, MAE, R² for regression). Understanding supervised learning is fundamental to most practical machine learning applications, as many real-world problems involve predicting outcomes based on historical data.