Clustering and classification are two common machine learning techniques for recognizing patterns in data. In the first season of our Lucid Thoughts explainer video series, we talked about what they are and how to tell the differences between them. Watch the episode now:
Classification is a type of supervised learning where the computer is learning to do something based on a set of training data that is already labeled by humans. The machine classifies new data according to these predetermined labels by finding patterns in the data set.
Clustering is a type of unsupervised learning so there is no training set or pre-existing classes or labels for the machine to work with. The machine looks at the various characteristics of the data set and finds what’s similar and what’s not.
Both methods teach the machine how to organize data, just in different ways.
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