Editing Dimensionality reduction (section)

==Feature selection==
{{Main|Feature selection}}{{See also|Combinatorial optimization}}
The process of [[feature selection]] aims to find a suitable subset of the input variables (''features'', or ''attributes'') for the task at hand. The three strategies are: the ''filter'' strategy (e.g., [[Information gain (decision tree)|information gain]]), the ''wrapper'' strategy (e.g., accuracy-guided search), and the ''embedded'' strategy (features are added or removed while building the model based on prediction errors).

[[Data analysis]] such as [[Regression analysis|regression]] or [[Statistical classification|classification]] can be done in the reduced space more accurately than in the original space.<ref>{{cite journal
 |first=Antonio |last=Rico-Sulayes
 |url=https://rielac.cujae.edu.cu/index.php/rieac/article/view/478
 |title=Reducing Vector Space Dimensionality in Automatic Classification for Authorship Attribution
 |journal=Revista Ingeniería Electrónica, Automática y Comunicaciones |issn=1815-5928
 |volume=38 |number=3 |pages=26–35 |year=2017
}}</ref>