Supervised learning
Training a model from features and their corresponding labels. Supervised machine learning is analogous to learning a subject by studying a set of questions and their corresponding answers. After mastering the mapping between questions and answers, a student can then provide answers to new (never-before-seen) questions on the same topic.1
Types
Evaluation
We evaluate a trained model to determine how well it learned. When we evaluate a model, we use a labeled dataset, but we only give the model the dataset’s features. We then compare the model’s predictions to the label’s true values. … Depending on the model’s predictions, we might do more training and evaluating before deploying the model in a real-world application.2
See also
- Unsupervised learning