Introduction to Supervised Learning
Explore the fundamental concepts of supervised learning, including how models are trained using labeled data, the roles of features and labels, and common algorithms for regression, classification, and ensemble methods. Learn how to evaluate model performance with metrics like accuracy and precision to build effective predictive models.
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. ...