4 papers analyzed
These studies suggest supervised machine learning uses labeled inputs for training, while unsupervised machine learning infers patterns from unlabeled data.
Supervised and unsupervised machine learning are two fundamental approaches in the field of artificial intelligence. Supervised learning involves training a model on labeled data, where the desired output is known, while unsupervised learning involves finding patterns and structures in data without pre-existing labels.
Definition and Process:
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Supervised machine learning relies on labeled data and is generally simpler and more accurate due to explicit training with input-output pairs. In contrast, unsupervised machine learning identifies patterns in unlabeled data, making it more complex and variable in performance. Each approach has its own set of methods and is suited to different types of tasks and data structures.
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