<prompt> Bolela likarolo tse peli tsa mochini oa vector oa tšehetso SVM.nan  <response>: Instruction in English: Name two features of a support vector machine SVM. . Response in English: 1. Maximum Margin Hyperplane: A support vector machine works by finding the optimal decision boundary that separates the data points of different classes by the widest possible margin; this boundary is called the maximum margin hyperplane. The distance between the hyperplane and the closest data points from either side is referred to as the margin.2. Support Vectors: The data points that are closest to the maximum margin hyperplane and contribute to defining the hyperplanes position and orientation are called the support vectors. These points are critical to the SVMs performance as removing any other points will not alter the position of the hyperplane, but removing a support vector would cause the hyperplane to shift its position. . Response in Sesotho: 1. Maximum Margin Hyperplane: Mochini oa vector oa tšehetso o sebetsa ka ho fumana moeli o nepahetseng oa liqeto o arolang lintlha tsa data tsa lihlopha tse fapaneng ka moeli o pharaletseng ka ho fetisisa; moeli ona o bitsoa maximum margin hyperplane. Sebaka se pakeng tsa hyperplane le lintlha tse haufi tsa data tse tsoang mahlakoreng ka bobeli li bitsoa margin. 2. Li-Vectors tsa Tsehetso: Lintlha tsa data tse haufi haholo le hyperplane ea marang-rang me li kenya letsoho ho hlalosa boemo ba hyperplane le mokhoa oa ho tsamaisa li bitsoa li-vectors tsa tšehetso. Lintlha tsena li bohlokoa tsebetsong ea SVM kaha ho tlosa lintlha tse ling ho ke ke ha fetola boemo ba hyperplane, empa ho tlosa vector ea tšehetso ho ka etsa hore hyperplane e fetole boemo ba eona. 