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[[Supervised learning]] involves the construction of a [[model]] from [[input]] data points and their associated [[output]] labels or classes. Machine learning [[algorithm]] utilizes this information to construct an algorithm that can accurately predict the class of new data points or inputs. Ultimately, the goal is to minimize the discrepancy between predicted classes and actual ones. | [[Supervised learning]] involves the construction of a [[model]] from [[input]] data points and their associated [[output]] labels or classes. Machine learning [[algorithm]] utilizes this information to construct an algorithm that can accurately predict the class of new data points or inputs. Ultimately, the goal is to minimize the discrepancy between predicted classes and actual ones. | ||
[[Unsupervised learning]] involves no output labels or classes; rather, the goal is to uncover patterns or clusters in data based on similarities or differences. In this scenario, a machine learning algorithm may group data points into distinct classes or clusters based on certain [[ | [[Unsupervised learning]] involves no output labels or classes; rather, the goal is to uncover patterns or clusters in data based on similarities or differences. In this scenario, a machine learning algorithm may group data points into distinct classes or clusters based on certain [[feature]]s or characteristics. | ||
==Types of classes== | ==Types of classes== | ||