Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In statistics, dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature extraction.Feature selection approaches try to find a subset of the original variables (also called features or attributes). Two strategies are filter (e.g. information gain) and wrapper (e.g. search guided by the accuracy) approaches. See also combinatorial optimization problems.In some cases, data analysis such as regression or classification can be done in the reduced space more accurately than in the original space.

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