<prompt> Ke boleng bofe bo botle ba hyperparameter gamma ho SVM?nan  <response>: Instruction in English: What is the best value for the hyperparameter gamma in SVM? . Response in English: The best value for the hyperparameter gamma in a support vector machine SVM depends on the specifics of the dataset being used and the problem being solved. Gamma defines the influence of a single training example on the decision boundary. A large value for gamma results in a more complex decision boundary, while a small value results in a smoother boundary. The most common method to find the best value for gamma is through a grid search with cross-validation, testing a range of possible values to find the one that results in the best model performance. Another method is randomized search, which selects random combinations of hyperparameters to evaluate.It is important to note that the best value for gamma is highly dependent on the data and the problem at hand, and there is no one-size-fits-all answer. . Response in Sesotho: Boleng bo molemo ka ho fetisisa bakeng sa gamma ea hyperparameter mochining oa vector ea tšehetso SVM e itšetlehile ka lintlha tse tobileng tsa dataset e sebelisoang le bothata bo rarolloa. Gamma e hlalosa tšusumetso ea mohlala o le mong oa koetliso moeling oa qeto. Boleng bo boholo ba gamma bo fella ka moeli oa liqeto o rarahaneng, athe boleng bo bonyenyane bo fella ka moeli o bonolo. Mokhoa o atileng haholo oa ho fumana boleng bo holimo bakeng sa gamma ke ka ho batla marang-rang ka netefatso, ho leka mefuta e mengata ea boleng ho fumana e hlahisang tsebetso e ntle ka ho fetisisa ea mohlala. Mokhoa o mong ke patlo e sa reroang, e khethang motsoako o sa reroang oa li-hyperparameter ho lekola. Ke habohlokoa ho hlokomela hore boleng bo molemo ka ho fetisisa bakeng sa gamma bo itšetlehile haholo ka data le bothata bo teng, me ha ho na karabo e lekanang-tsohle. 