Introduction to Supervised Learning

Supervised learning is a fundamental branch of ML that deals with training models on labeled data. It encompasses various algorithms and techniques that aim to predict or estimate a target variable based on input features and known corresponding outcomes. By leveraging the relationships between input variables and their associated labels, supervised learning enables the development of models that can generalize and make accurate predictions on unseen data.

Key concepts in supervised learning

Supervised learning involves algorithms learning from labeled data in order to predict outcomes. Data is divided into features (measurable attributes) and labels (desired outcomes). The training set teaches the model, and the test set evaluates its predictions. Optimization aims to minimize the difference between predicted and true labels. ...