In machine learning, understanding the difference between supervised and unsupervised learning is fundamental. Supervised learning involves training a model on a labeled dataset, where the output is known.
In contrast, unsupervised learning deals with data that does not have labeled responses. The model attempts to find patterns and relationships within the data without prior knowledge of the outcomes.
Recognizing whether a problem requires supervised or unsupervised learning is crucial for effective model selection and achieving desired results.