Explain machine learning concepts in simple terms

Disclaimer

I use ChatGPT to create this ELI5 collection as a research into Human - Large Language Model collaboration. See methodology here.

I do not claim authorship nor guarantee the correctness of this article. However, I did put in efforts to make it correct and helpful in key aspects (see more in the methodology, link above).

less than 1 minute read

A support vector machine (SVM) is a type of machine learning model that is used to classify things into categories. It works by finding the line or plane that best separates the categories. The points closest to the line or plane are called support vectors, and they are used to define the boundary between the categories.

For example, imagine you are trying to classify animals into two categories: mammals and non-mammals. An SVM would find the line or plane that best separates the mammals from the non-mammals. The points closest to the line or plane would be the support vectors, and they would be used to define the boundary between the two categories.

SVMs are useful because they can handle complex data and they can accurately classify things into categories. They are often used in applications such as image recognition and spam detection.

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