<prompt> Hlalosa sepheo sa algorithm e latelang ea komporoK-haufinyane algorithm ea Moahelani  <response>: Instruction in English: Describe the purpose of the following computer algorithm . Response in English: The purpose of the K-nearest Neighbor KNN algorithm is to classify new data points based on their similarity to known and labeled data points. It is a supervised machine learning algorithm that can be used to solve classification and regression problems. In the case of a classification problem, the KNN algorithm works by finding the K-nearest labeled data points or neighbors to the new, unlabeled data point. Then, it assigns the most common label among these K neighbors to the new data point. In a regression problem, the algorithm works similarly, but it calculates the average value of the target variable among the K nearest neighbors to predict the value for the new data point.The distance between data points is calculated using a distance metric, such as Euclidean distance or Manhattan distance. The choice of K can impact the performance of the algorithm, where a small value of K can result in overfitting and a large value can result in underfitting. Hence, the selection of the right value of K is important for achieving good results. One of the major advantages of the KNN algorithm is its simplicity and ease of implementation. Additionally, it can adapt to changes in the data, making it a good choice for applications where new data is constantly being added. However, it can be computationally expensive for large datasets since it requires calculating the distance between the new data point and all other labeled data points, and it may not work well with high dimensional data since the distances between data points become less meaningful in higher dimensions. . Response in Sesotho: Sepheo sa algorithm ea K-Nearest Neighbor KNN ke ho hlophisa lintlha tse ncha tsa data ho latela ho tšoana ha tsona le lintlha tsa data tse tsejoang le tse ngolisitsoeng. Ke algorithm ea ho ithuta ka mochini e hlokometsoeng e ka sebelisoang ho rarolla mathata a ho hlopha le ho khutlisa. Tabeng ea bothata ba ho hlophisa, algorithm ea KNN e sebetsa ka ho fumana lintlha tsa data tse ngolisitsoeng haufi le K kapa baahelani sebakeng se secha, se sa ngolisoang. Ebe, e fana ka label e atileng haholo hara baahisani bana ba K sebakeng se secha sa data. Bothateng ba ho khutlela morao, algorithm e sebetsa ka mokhoa o tsoanang, empa e lekanya boleng ba karolelano ea sepheo se fapaneng hara baahisani ba haufi ba K ho bolela esale pele boleng ba ntlha e ncha ea data. Sebaka se pakeng tsa lintlha tsa data se baloa ho sebelisoa metric ea sebaka, joalo ka sebaka sa Euclidean kapa sebaka sa Manhattan. Khetho ea K e ka ama tsebetso ea algorithm, moo boleng bo bonyenyane ba K bo ka fellang ka ho fetella me boleng bo boholo bo ka fella ka ho fokotseha. Kahoo, khetho ea boleng bo nepahetseng ba K ke ea bohlokoa bakeng sa ho fumana litholoana tse ntle. E ngoe ea melemo e meholo ea algorithm ea KNN ke ho ba bonolo le bonolo ho e kenya tsebetsong. Ho feta moo, e ka ikamahanya le liphetoho tsa data, ea e etsa khetho e ntle bakeng sa litsebetso moo data e ncha e ntseng e eketsoa khafetsa. Leha ho le joalo, e ka ba e theko e boima haholo bakeng sa li-dataset tse kholo kaha e hloka ho bala sebaka se pakeng tsa lintlha tse ncha tsa data le lintlha tse ling tsohle tse ngolisitsoeng, me e ka na ea se ke ea sebetsa hantle ka lintlha tse phahameng ho tloha ha libaka tse pakeng tsa lintlha tsa data li se na thuso ka litekanyo tse phahameng. . 