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Explaining the Difference Between Supervised and Unsupervised Learning

In supervised learning, the algorithm learns from labeled training data, where each data point is accompanied by the correct answer. The algorithm then uses this labeled data to make predictions or classify new data. Popular examples of supervised learning include classification and regression tasks, such as email spam detection or predicting housing prices.

On the other hand, unsupervised learning involves training the algorithm on unlabeled data, where the algorithm tries to find hidden patterns or structures in the data without explicit guidance. Examples of unsupervised learning include clustering and association tasks, like market segmentation or anomaly detection.

When to use supervised learning:

• When there is a clearly defined output or target variable
• When the goal is to predict or classify new data based on past observations

When to use unsupervised learning:

• When there is no labeled data available
• When exploring the underlying structure of the data is the primary objective

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