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Hands-On Machine Learning with C# by Matt R. Cole

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Unsupervised learning

Contrary to supervised learning, unsupervised usually has more leeway in how the outcome is determined. The data is treated such that, to the algorithm, there is no single feature more important than any other in the dataset. These algorithms learn from datasets of input data without the expected output data being labeled. k-means clustering (cluster analysis) is an example of an unsupervised model. It is very good at finding patterns in the data that have meaning relative to the input data. The big difference between what we learned in the supervised section and here is that we now have x features X1, X2, X3, ... Xx measured on n observations. But we no longer interested in prediction of Y because we no longer have ...

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