<prompt> Hlalosa hore na ho netefatsoa ke eng, hona hobaneng ho le bohlokoa?nan  <response>: Instruction in English: Explain what is cross-validation and why is it important? . Response in English: Cross-validation is a statistical method used in machine learning to assess the performance of a predictive model on an independent data set. The goal of cross-validation is to evaluate how well a model will generalize to new data that it has not seen before. In cross-validation, a data set is split into two parts: a training set and a validation set. The training set is used to train the model, while the validation set is used to assess the performance of the model. The performance of the model on the validation set provides an estimate of its true performance on new data. The most common form of cross-validation is k-fold cross-validation, where the data set is divided into k equally sized subsets or folds. The model is then trained on k-1 folds and tested on the remaining fold. This process is repeated k times, with each fold serving as the validation set once. The average performance across all k folds is used to evaluate the model. Cross-validation is important for several reasons. Firstly, it allows us to estimate the true performance of a model on new data, which is essential for choosing the best model for a particular task. Secondly, it helps to prevent overfitting, which occurs when a model is too complex and fits the training data too well, including the noise and random fluctuations in the data. Overfitting can result in poor performance when the model is applied to new data. Cross-validation helps to detect overfitting by showing how well the model performs on an independent data set. Finally, cross-validation can be used to select the best parameters for a model, by evaluating the performance of the model for different values of the parameters.In summary, cross-validation is an essential technique in machine learning for evaluating the performance of predictive models and selecting the best model for a particular task. It helps to prevent overfitting and to select the best parameters for a model, thus improving the models ability to generalize to new data. . Response in Sesotho: Cross-validation ke mokhoa oa lipalo-palo o sebelisoang thutong ea mochini ho lekola tsebetso ea mohlala o boletsoeng esale pele ho sete ea data e ikemetseng. Sepheo sa ho netefatsa ke ho lekola hore na mohlala o tla akaretsa lintlha tse ncha hakae tseo e neng e sa li bone pele. Ha ho netefatsoa, sete ea data e arotsoe likarolo tse peli: setsi sa koetliso le sete sa netefatso. Sehlopha sa koetliso se sebelisetsoa ho koetlisa mohlala, ha setsi sa ho netefatsa se sebelisetsoa ho hlahloba tsebetso ea mohlala. Tsebetso ea mohlala ho sete ea netefatso e fana ka khakanyo ea tsebetso ea eona ea nete ho data e ncha. Mokhoa o atileng haholo oa netefatso ea sefapano ke netefatso ea k-fold cross-validation, moo data e arotsoeng ka k kapa mameno a boholo bo lekanang. Joale mohlala o koetlisetsoa ka mameno a k-1 ebe o lekoa lesakeng le setseng. Tsebetso ena e phetoa makhetlo a k, me lemeno ka leng le sebetsa joalo ka netefatso e behiloeng hang. Kakaretso ea tsebetso ho menahano eohle ea k e sebelisoa ho lekola mohlala. Ho netefatsoa ka sefapano ho bohlokoa ka mabaka a maloa. Taba ea pele, e re lumella ho hakanya tsebetso ea nete ea mohlala ho data e ncha, e leng bohlokoa bakeng sa ho khetha mohlala o motle bakeng sa mosebetsi o itseng. Taba ea bobeli, e thusa ho thibela ho fetella, ho etsahalang ha mohlala o rarahane haholo mme o lumellana hantle le data ea koetliso, ho kenyelletsa le lerata le ho feto-fetoha ha data ka tšohanyetso. Ho feta tekano ho ka baka tsebetso e mpe ha mohlala o sebelisoa ho data e ncha. Ho netefatsa ho thusa ho lemoha ho fetella ka ho bontsa hore na mohlala o sebetsa hantle hakae ho sete ea data e ikemetseng. Qetellong, netefatso ea sefapano e ka sebelisoa ho khetha li-parameter tse ntle ka ho fetisisa bakeng sa mohlala, ka ho hlahloba tsebetso ea mohlala bakeng sa litekanyetso tse fapaneng tsa litekanyetso. Ka bokhutsoane, netefatso ea sefapano ke mokhoa oa bohlokoa thutong ea mochini bakeng sa ho lekola tsebetso ea mefuta e boletsoeng esale pele le ho khetha mohlala o motle ka ho fetisisa bakeng sa mosebetsi o itseng. E thusa ho thibela ho fetella le ho khetha li-parameter tse ntle ka ho fetisisa bakeng sa mohlala, ka hona ho ntlafatsa bokhoni ba mohlala ba ho akaretsa lintlha tse ncha. 