Supervised Learning
Supervised learning is a type of machine learning where the model is trained on labeled examples: input-output pairs. The model learns to predict the correct output (label) for new inputs.
In Simple Terms
Think of it as learning from answer keys: the model sees many questions with correct answers and learns to match that pattern.
Detailed Explanation
Common tasks include classification (e.g., spam or not) and regression (e.g., predicting a number). The training data must be labeled; quality and quantity of labels strongly affect performance. Supervised learning is well-understood and widely used in production. It contrasts with unsupervised learning (no labels) and reinforcement learning (reward signal). Many real-world applications start with supervised models and then add other paradigms as needed.
Related Terms
Artificial Intelligence
The simulation of human intelligence processes by machines, especially computer systems.
Read moreTransfer Learning
Transfer learning is the practice of taking a model already trained on one task or dataset and reusing or adapting it for another. It speeds development and often improves performance when data for the new task is limited.
Read moreMachine Learning
A subset of AI that enables systems to learn and improve from experience without being explicitly programmed.
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