<prompt> Algorithm ea K-Nearest Neighbor ke eng?nan  <response>: Instruction in English: What is a K-Nearest Neighbor algorithm? . Response in English: The K-Nearest Neighbor KNN algorithm is a simple, easy-to-implement supervised learning algorithm that can be used to solve both classification and regression problems. It is a type of memory-based learning in which an object is classified by a majority vote from its nearest neighbors, with the object being assigned to the class most common among its K nearest neighbors. K is a positive integer, typically small, and is a parameter that is used to determine the number of neighbors that contribute to the classification or prediction of a new observation. The theory behind the KNN algorithm is based on the assumption that similar things exist in close proximity, and it functions on the principle of calculating the distance between the new observation and all the existing observations, and then selecting the K-closest observations to make a prediction. . Response in Sesotho: Algorithm ea K-Nearest Neighbor KNN ke algorithm e bonolo, e bonolo ho e kenya tsebetsong ea ho ithuta e hlokometsoeng e ka sebelisoang ho rarolla mathata a mabeli a lihlopha le a ho khutlela morao. Ke mofuta oa thuto e thehiloeng mohopolong moo ntho e khetholloang ka likhetho tse ngata ho tsoa ho baahisani ba eona ba haufi, me ntho e abeloa sehlopha se tloaelehileng hara baahisani ba K ba haufi. K ke palo e felletseng, hangata e nyane, me ke paramethara e sebelisetsoang ho fumana palo ea baahisani ba kenyang letsoho ho hlopheng kapa ho bolela esale pele tebello e ncha. Khopolo-taba ea algorithm ea KNN e thehiloe khopolong ea hore lintho tse tšoanang li teng haufi-ufi, me e sebetsa ho latela molao-motheo oa ho bala sebaka se pakeng tsa pono e ncha le lintho tsohle tse hlokometsoeng, ebe o khetha maikutlo a haufi-ufi a K. ho bolela esale pele. 