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Supervised vs. Unsupervised Learning in Machine Learning

In machine learning, supervised learning involves training a model on labeled data, where the algorithm learns to map input data to the correct output based on example input-output pairs. This type of learning requires a labeled dataset for training, and the model aims to generalize to unseen data accurately.

On the other hand, unsupervised learning does not require labeled data for training. Instead, the algorithm explores the data and identifies patterns or structures without explicit guidance. Unsupervised learning is often used for clustering, dimensionality reduction, and anomaly detection.

Overall, the main difference between supervised and unsupervised learning is the presence of labeled data in supervised learning, while unsupervised learning operates on unlabeled data to discover hidden patterns or relationships.

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