<prompt> Bapisa le ho bapisa netefatso ea sefapano le tlhahlobo e kantle ho mehlala.nan  <response>: Instruction in English: Compare and contrast cross-validation and out-of-sample testing. . Response in English: Cross-validation and out-of-sample testing are both techniques used to estimate the predictive power of a model on unseen data. However, there are some key differences between the two methods.Cross-validation is a technique which involves dividing the dataset into k subsets folds, training the model on k-1 of the subsets, and then testing the model on the remaining subset. This process is repeated k times, with a different subset being held out for testing each time. The results of each test are then averaged, giving an overall estimate of the models performance.Out-of-sample testing, on the other hand, involves holding out a portion of the dataset often referred to as the test set at the beginning of the modeling process. The model is then trained on the remaining data often referred to as the training set, and its performance is evaluated on the held-out test set.One key difference between the two methods is that cross-validation makes more efficient use of the available data, as it uses each observation for both training and testing. This can be beneficial when the dataset is relatively small. Out-of-sample testing, on the other hand, uses a portion of the data exclusively for testing, meaning that this data is not available for training. This can result in a less powerful model if the training set is significantly reduced in size.Another important difference is that cross-validation provides a more robust estimate of model performance, as it averages the results of multiple tests. This can help to reduce the impact of potential biases or anomalies in the data. Out-of-sample testing, on the other hand, provides a single estimate of model performance, which may be less reliable if the test set is not representative of the population as a whole.In summary, both cross-validation and out-of-sample testing are useful techniques for evaluating the predictive power of a model. Cross-validation is often preferred when the dataset is small or when a robust estimate of model performance is required, while out-of-sample testing can be a simpler and quicker option when the dataset is large enough to allow for the creation of a representative test set. . Response in Sesotho: Tlhahlobo ea ho netefatsa le ho tsoa ho mehlala ke mekhoa e meli e sebelisoang ho hakanya matla a ho bolela esale pele a mohlala ho data e sa bonahaleng. Leha ho le joalo, ho na le phapang e itseng ea bohlokoa pakeng tsa mekhoa e meli. Cross-validation ke mokhoa o kenyelletsang ho arola dataset ka k subsets memeno, ho koetlisa mohlala ho k-1 ea likaroloana, ebe ho leka mohlala ho sehlopha se senyenyane se setseng. Tsebetso ena e phetoa makhetlo a k, me karoloana e fapaneng e tsoareloa tlhahlobo nako le nako. Joale liphetho tsa tlhahlobo e ngoe le e ngoe li lekanngoa, ho fana ka khakanyo e akaretsang ea tsebetso ea mohlala. Ka lehlakoreng le leng, tlhahlobo e tsoang ka ntle ho sampuli e kenyelletsa ho tšoara karolo ea dataset hangata e bitsoang test set qalong ea mokhoa oa ho etsa mohlala. Joale mohlala o koetlisetsoa ho data e setseng hangata e bitsoa setsi sa koetliso, me tsebetso ea eona e hlahlojoa ka sete ea teko e tšoaretsoeng. Phapang e ngoe ea bohlokoa lipakeng tsa mekhoa ena e meli ke hore netefatso ea sefapano e sebelisa hantle lintlha tse teng, kaha e sebelisa tlhokomeliso ka ngoe bakeng sa koetliso le tlhahlobo. Sena se ka ba molemo ha dataset e batla e le nyane. Ka lehlakoreng le leng, tlhahlobo e tsoang kantle ho sampuli e sebelisa karolo ea data bakeng sa tlhahlobo feela, ho bolelang hore data ena ha e fumanehe bakeng sa koetliso. Sena se ka fella ka mohlala o seng matla haholo haeba sete sa koetliso se fokotsehile haholo ka boholo. Phapang e ngoe ea bohlokoa ke hore netefatso ea sefapano e fana ka khakanyo e matla ea tsebetso ea mohlala, kaha e lekanya liphetho tsa liteko tse ngata. Sena se ka thusa ho fokotsa litlamorao tsa leeme kapa liphoso tse ka bang teng ho data. Ka lehlakoreng le leng, tlhahlobo e tsoang ka ntle ho sampuli e fana ka tekanyo e le ngoe ea tsebetso ea mohlala, e ka nang ea se ke ea tšepahala haeba tlhahlobo ea tlhahlobo e sa emele baahi ka kakaretso. Ka kakaretso, tlhahlobo ea ho netefatsa le ho tsoa ho sampole ke mekhoa e sebetsang ea ho lekola matla a ho bolela esale pele a mohlala. Hangata ho khethoa ha dataset e nyane kapa ha ho hlokahala khakanyo e matla ea tsebetso ea mohlala, athe tlhahlobo ea kantle ho sampuli e ka ba khetho e bonolo le e potlakileng ha dataset e le kholo ho lekana ho lumella ho theha moemeli. teko sete. 