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Asked by Ashwini Last Modified
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Supervised learning uses labeled data (input-output pairs) to train a model, while semi-supervised learning uses a combination of labeled and unlabeled data, often improving performance by leveraging both types of information. It's like having some answers and trying to find more by using clues from the questions you don't know.
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