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Supervised learning trains a statistical model using labeled input–output pairs.
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).
Supervised learning trains a statistical model using labeled input–output pairs.
The goal is accurate prediction on new, unseen data via generalization (measured by generalization error).
Common supervised tasks include classification and regression.
A machine learning paradigm where an algorithm learns to map inputs to outputs using labeled example pairs.
Training data where each input is provided with its correct output (label/target).
A measure of how well a trained model performs on new, unseen data compared to the training data.
A supervised learning task that predicts discrete categories (e.g., spam vs. not spam).
A supervised learning task that predicts continuous values (e.g., house prices).
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