<prompt> Hlalosa hore na lisebelisoa tsa data li sebelisoa joang thutong ea mochini.nan  <response>: Instruction in English: Explain how data sets are used in machine learning. . Response in English: A dataset is defined as a collection of data, which can come in various forms, such as tabular or matrix data, image data, text, audio, or video data. The purpose of a dataset is to act as a medium for training, testing, and validation in machine learning.When working with machine learning models or algorithms, a dataset is typically divided into three parts: first, the training dataset, which is used to train the model by adjusting weights, biases, and other model components. Second, the validation dataset, which is used to tune model hyperparameters, check for convergence, and to decide whether the model is memorizing the training data or generalizing well to new data. Finally, the testing dataset, which is used to assess how well the final model behaves on new data that it has never seen.The data within these sets are used to provide information, or features, about each data point so that the model can identify and learn patterns within the data. During training, the machine learning algorithm attempts to learn a mapping function between the input features and the outputs, through an iterative process of repeating the training and validation process using different combinations of weights and hyperparameters. After the model is trained and optimized, the test data is used to measure how accurately the model predicts the outcomes based on previously unseen data.In summary, datasets are an essential piece for training, validating and testing the accuracy of machine learning models. Without them, models would not have historical data to learn from and make predictions. . Response in Sesotho: Lethathamo la data le hlalosoa e le pokello ea data, e ka tlang ka mefuta e fapaneng, joalo ka data ea tabular kapa matrix, data ea litšoantšo, mongolo, audio kapa video. Morero oa pokello ea data ke ho sebetsa joalo ka mokhoa oa ho koetlisa, ho etsa liteko, le ho netefatsa thuto ea mochini. Ha o sebetsa ka mefuta ea ho ithuta ka mochini kapa li-algorithms, pokello ea data hangata e arotsoe ka likarolo tse tharo: ea pele, pokello ea data ea koetliso, e sebelisetsoang ho koetlisa mohlala ka ho lokisa boima, leeme, le likarolo tse ling tsa mohlala. Taba ea bobeli, dataset ea netefatso, e sebelisetsoang ho hlophisa li-hyperparameter tsa mohlala, ho lekola convergence, le ho etsa qeto ea hore na mohlala o tšoara ka hlooho lintlha tsa koetliso kapa o akaretsa hantle ho data e ncha. Qetellong, dataset ea tlhahlobo, e sebelisetsoang ho hlahloba hore na mohlala oa ho qetela o itšoara hantle hakae ho data e ncha eo e e-so ka e e bona. Lintlha tse ka hara lihlopha tsena li sebelisetsoa ho fana ka tlhahisoleseding, kapa likarolo, mabapi le ntlha e ngoe le e ngoe ea data e le hore mohlala o tsebe ho tseba le ho ithuta mekhoa ka hara data. Nakong ea koetliso, algorithm ea ho ithuta mochine e leka ho ithuta mosebetsi oa mapa pakeng tsa likarolo tsa ho kenya le liphetho, ka mokhoa o pheta-phetoang oa ho pheta-pheta mokhoa oa ho koetlisa le ho netefatsa o sebelisa mefuta e fapaneng ea litekanyo le li-hyperparameter. Ka mora hore mohlala o koetlisoe le ho ntlafatsoa, lintlha tsa teko li sebelisetsoa ho lekanya hore na mohlala o bolela esale pele ka nepo liphello tse thehiloeng boitsebisong bo neng bo e-so bonoe pele. Ka bokhutšoanyane, li-datasets ke karolo ea bohlokoa bakeng sa koetliso, ho netefatsa le ho leka ho nepahala ha mefuta ea ho ithuta ea mochine. Ntle le bona, mehlala e ne e ke ke ea ba le data ea histori eo u ka ithutang eona le ho bolela esale pele. 