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dimensionality reduction
In machine learning and statistics, dimensionality reduction or 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==
(詳細はFeature selection approaches try to find a subset of the original variables (also called features or attributes). There are three strategies; ''filter'' (e.g. information gain) and ''wrapper'' (e.g. search guided by accuracy) approaches, and ''embedded'' (features are selected to add or be removed while building the model based on the prediction errors). 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.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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