Supervised Learning Learning Path

Supervised learning definition and goal

Supervised learning is a machine learning paradigm where an algorithm learns a mapping from input data to output values using labeled examples (input–output pairs). The “supervised” aspect comes from a teacher-like role: the training data includes the correct answers, guiding the model toward making accurate predictions. The goal of supervised learning is to generalize—i.e., to predict the correct outputs for new, unseen data. This is evaluated using generalization error, and the approach is commonly applied to classification tasks (predicting categories) and regression tasks (predicting continuous values).

0/2 modes complete

Training data types and labeled examples

0/2 modes complete