Supervised Learning shows AutoML algorithms sets of known outcomes from which to learn. Think of classroom drills, or giving a bloodhound the scent.
Supervised learning relies on training data containing labels for data columns (features). Known outcomes must be included for the columns to be predicted on fresh data.
Classifications where there are two (binary) or more (multi-class) possibilities are use cases for supervised learning . Regressions—predicting scalar numerical values such as forecasts—are also suited to supervised learning.
Unsupervised learning happens when AutoML algorithms are given unlabeled training data and must make sense of it without any instruction. Such machines “teach themselves” what result to produce.
Unsupervised learning algorithms look for structure in the training data, like finding which examples are similar to each other and grouping them into clusters.
Clustering, market basket analyses, and anomaly detection are common use cases for unsupervised learning.
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