<prompt> Hlahisa mohlala bakeng sa mohopolo oa hyperparameter tuning.nan  <response>: Instruction in English: Generate an example for the concept of hyperparameter tuning. . Response in English: Hyperparameter tuning is the process of finding the best settings for the model, allowing it to learn well from the data and perform better in finding a solution to the particular problem. For example, suppose we are using a machine learning algorithm called the k-nearest neighbor k-NN to classify whether patients who have undergone some medical tests have a particular disease, based on their test results.One of the main parameters of the k-NN algorithm is the number k, which represents the number of the nearest neighbors the algorithm considers when making predictions. Supposedly, finding the optimal k is vital in achieving an accurate model.When performing hyperparameter tuning, a modeler would experiment with different values of k to see a range of settings, such as k1, k5, and k10. They would then evaluate the algorithms performance, typically through a measure like accuracy, for each of these values of k. Once the modeler determines the best value of k, they would set that k value as the final hyperparameter setting and use the model with the newly optimized value to make predictions on unseen data. . Response in Sesotho: Hyperparameter tuning ke mokhoa oa ho fumana litlhophiso tse ntle ka ho fetisisa tsa mohlala, ho o lumella ho ithuta hantle ho tsoa ho data le ho sebetsa hantle ho fumana tharollo bothateng bo itseng. Mohlala, ha re re re sebelisa algorithm ea ho ithuta ka mochini e bitsoang k-nearest neighbour k-NN ho hlakisa hore na bakuli ba entseng liteko tse ling tsa bongaka ba na le lefu le itseng, ho latela liphetho tsa liteko tsa bona. E ngoe ea likarolo tse ka sehloohong tsa algorithm ea k-NN ke nomoro k, e emelang palo ea baahelani ba haufi eo algorithm e nahanang ka eona ha e bolela esale pele. Ho thoe, ho fumana k e nepahetseng ho bohlokoa ho fihlela mohlala o nepahetseng. Ha a etsa tokiso ea hyperparameter, moetsi oa mohlala o tla leka ka litekanyetso tse fapaneng tsa k ho bona mefuta e mengata ea litlhophiso, joalo ka k1, k5, le k10. Joale ba ne ba tla lekola tšebetso ea algorithm, hangata ka tekanyo e kang ho nepahala, bakeng sa e ngoe le e ngoe ea litekanyetso tsena tsa k. Hang ha moetsi oa mohlala a fumana boleng bo botle ka ho fetisisa ba k, ba tla beha boleng ba k joalo ka boemo ba ho qetela ba hyperparameter mme ba sebelise mohlala ka boleng bo sa tsoa ntlafatsoa ho etsa likhakanyo ka data e sa bonoeng. 