Supervised Learning

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In supervised learning, each training record pairs features with a known target (e.g., “email → spam/not spam,” “image → cat/dog,” “attributes → price”). The model studies many of these pairs and learns how to predict the label for new inputs it hasn’t seen before.

What makes supervised learning different from other learning types is that the target is provided during training. In unsupervised learning, the data has no labels, and the system looks for structure on its own. In reinforcement learning, the system learns by taking actions and receiving rewards over time. With supervised learning, training is measured by how well predictions match known targets, then checked on unseen data to ensure the model generalizes instead of memorizing. This approach is commonly used for classification and regression problems in real products.

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