Imagine we have a set of labeled and unlabeled data, and we want to build a classifier which takes the unlabeled data as input and labels that data as output.
With this kind of situation, we’ll need to build a classification model that will learn from already-labeled data (training data). Later we’ll use that model to predict our unlabeled data (test data).
This type of machine learning is called supervised learning, which we can define as feeding data into a machine learning algorithm.
In doing so, we’re actually showing that groups exist, and which data belong to which groups.
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