What is the difference between supervised and unsupervised learning?

Experience Level: Junior
Tags: Machine learning


Supervised learning involves training a machine learning model on a labeled dataset, where the input data is paired with the correct output. The goal is to learn a mapping from the input to the output so that the model can make accurate predictions on new, unseen data.

Examples of supervised learning include

  • image classification,
  • speech recognition,
  • regression analysis.

Unsupervised learning involves training a model on an unlabeled dataset, with the goal of discovering patterns or structure in the data.

Examples of unsupervised learning include

  • clustering,
  • dimensionality reduction,
  • anomaly detection.
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