Core Concepts
Supervised Learning
In supervised learning, every training example consists of an input paired with the correct output label. The model learns a mapping from inputs to outputs by minimizing prediction error across the labeled dataset. Common tasks include classification and regression.
It underpins many commercial AI applications—spam filters, fraud detection, medical diagnosis, and sentiment analysis. Quality and quantity of labeled data are the primary constraints.
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Related Terms
Core Concepts
Unsupervised Learning
Models learn patterns from unlabeled data without explicit instructions.
Miscellaneous
Training Data
The labeled or unlabeled dataset used to fit a model's parameters during the learning process.
Miscellaneous
Label
Annotation indicating the correct output or category for a training example in supervised learning.
Techniques & Methods
Fine-Tuning
Continuing the training of a pre-trained foundation model on a smaller, curated dataset to specialize its behavior, style, or domain expertise without losing its general capabilities.