Table 3.

Attributes of the 3 major categories of machine learning

Attribute	Supervised learning	Unsupervised learning	Reinforcement learning
Labelling	Data outcomes are labeled beforehand	Data outcomes are not labeled	Data outcomes are not labeled
Description	Algorithm makes prediction of outcomes based on predictors using labeled data as a reference	Algorithm is used to separate data into clusters	Algorithm is used to build a policy that maximizes a cumulative reward
Evaluation Metrics	Algorithms are evaluated based on area under the curve and accuracy relative to the ‘ground truth’ (i.e. the true values of the outcomes)	Difficult to evaluate algorithm performance in the absence of ‘ground truth’ data	Algorithms are evaluated based on cumulative reward
Examples	Examples include classification algorithms (e.g., support vector machine) and regression algorithms (e.g., regression tree)	Examples include k-means clustering and principal component analysis	Examples include Q-learning