<prompt> Akaretsa algorithm ea ho ithuta e hlokometsoeng ea KNN.nan  <response>: Instruction in English: Summarize the supervised learning algorithm KNN. . Response in English: K-Nearest Neighbors, or KNN for short, is a supervised learning algorithm used for classification and regression tasks. It works on the principle of assigning a new data point the same label as its closest neighbors in the training dataset. The number of neighbors examined, k, is a user-specified parameter that defines how many neighbors to include in the voting process. In a classification problem, the algorithm selects the class that is the most common among the k-nearest neighbors. In a regression problem, the algorithm uses the average of the k-nearest neighbors values as the output for the new data point. KNN is non-parametric, meaning it doesnt make any assumptions about the underlying data. It is also known as lazy learning, as it doesnt require any training, but instead, stores the entire dataset and uses it to make predictions at runtime. . Response in Sesotho: Baahelani ba K-Nearest, kapa KNN ka bokhutsoane, ke algorithm ea ho ithuta e hlokometsoeng e sebelisetsoang ho hlophisa le ho fokotsa mesebetsi. E sebetsa ka molao-motheo oa ho abela ntlha e ncha ea data label e tšoanang le baahelani ba eona ba haufi ka hara dataset ea koetliso. Palo ea baahisani ba hlahlobiloeng, k, ke paramethara e boletsoeng ke mosebelisi e hlalosang hore na ho kenyeletsoa baahelani ba bakae nakong ea ho vouta. Bothateng ba likarolo, algorithm e khetha sehlopha se atileng haholo hara baahelani ba k-haufi. Bothateng ba ho khutlela morao, algorithm e sebelisa karolelano ea litekanyetso tsa baahelani ba k-haufi e le tlhahiso ea ntlha e ncha ea data. KNN ha e na parametric, ho bolelang hore ha e etse maikutlo mabapi le lintlha tse ka tlase. E boetse e tsejoa e le ho ithuta ka botsoa, kaha ha e hloke koetliso leha e le efe, empa ho e-na le hoo, e boloka dataset eohle mme e e sebelisa ho etsa likhakanyo ka nako ea nako. 