What is the difference between supervised and unsupervised learning?

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Supervised learning and unsupervised learning are two major types of machine learning, differing mainly in the presence or absence of labeled data.

In supervised learning, the model is trained on a labeled dataset, meaning each training example is paired with a correct output. The goal is to learn a mapping from inputs to outputs so that it can predict the label for new, unseen data. Common tasks include classification (e.g., spam detection, where emails are labeled as "spam" or "not spam") and regression (e.g., predicting house prices based on features like size and location).

In contrast, unsupervised learning involves training a model on data without labeled responses. The system tries to learn the patterns or structure from the input data alone. Typical tasks include clustering (e.g., customer segmentation based on purchasing behavior) and dimensionality reduction (e.g., compressing data or visualizing high-dimensional data in 2D).

In summary, supervised learning uses labeled data to make predictions, while unsupervised learning explores the data to find hidden patterns without labels. Both are essential for different types of problems in machine learning.

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