--- title: What do you understand by feature vectors? description: What do you understand by feature vectors? --- [Skip to content](https://help.datasciencedojo.com/migration/what-do-you-understand-by-feature-vectors#main-content) English Show submenu for translations [Customer portal](https://help.datasciencedojo.com/tickets-view?hsLang=en) ![DSD main logo.png\]](https://help.datasciencedojo.com/hs-fs/hubfs/DSD%20main%20logo.png?height=40&name=DSD%20main%20logo.png) - [Default HubSpot Blog](https://help.datasciencedojo.com/blog) Open main navigation Close main navigation - [Default HubSpot Blog](https://help.datasciencedojo.com/blog) - English Show submenu for translations - [Customer portal](https://help.datasciencedojo.com/tickets-view) - Go to datasciencedojo.com Go to datasciencedojo.com How can we help? - There are no suggestions because the search field is empty. 1. [Help Center](https://help.datasciencedojo.com/migration?hsLang=en) 2. [Machine Learning](https://help.datasciencedojo.com/migration/machine-learning?hsLang=en) # What do you understand by feature vectors? We typically rely on computer features to extract useful information for the prediction of another function, assuming that they have a static or non-linear relationship. The output of the built machine learning model will demonstrate the validity of this statement.   Feature vector is an n-dimensional vector of numerical features that describe some object in pattern recognition in machine learning Many machine learning algorithms rely on numerical representations of objects because they make processing and statistical analysis easier. A vector is nothing more than a list of numerical values. It’s clear that a vector is simply a list of a feature’s calculated values. 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