Is number of features the same as number of dimentions in SVM?

can we say that if we have two features the number of dimension is two, three features means three-dimensional, an so on?

and will number of classes affect it?

I'm using SVM to classify my data into two classes, with three features. how many dimensional does my feature environment have?

Answer 1

[Walter Roberson]

For SVM and most other clustering methods, features correspond to columns, and rows correspond to samples. If you have N features then there is a sense in which you are exploring an N-dimensional space mathematically. However, you need to distinguish between that meaning of the word "dimension" and the typical MATLAB meaning of the word as referring to the number of indices in the indexing expression.