FIGURE 2.

Unsupervised ML schematic. As with supervised learning, a training data set undergoes automated feature extraction, with this information being analyzed by the selected ML algorithm. Unlike supervised learning, there are no manually labeled outputs to “train” and refine prediction. Instead, data are analyzed for intrinsic patterns, and through iterative adjustment input data, increasingly refined cutoffs for distinct clusters within the data can be generated.