<prompt> Hlalosa tsebetso ea ho netefatsa ho fapana ha thuto ea mochininan  <response>: Instruction in English: Describe the process of cross-validation in machine learning . Response in English: Cross-validation is a technique used in machine learning to assess the predictive performance of a model, especially when you have a small amount of data. Its used to estimate how accurately a predictive model will perform in practice.The process usually goes as follows:1. Splitting the data: The first step is dividing the dataset into two parts: one for training the model and the other for testing it. However, in cross-validation, the data is further split into K folds, where K is a predefined number usually between 5-10.2. Training and Testing: Then, the model is trained on K-1 subsets or folds of the data and validated on the remaining one subset. This process is repeated K times, with a different fold used for validation each time.3. Evaluate the performance: Model performance is determined by averaging the performance scores on the K-folds. This provides a more accurate measure of how well the model performs on unseen data.4. Select the best Model: Cross-validation can be used to compare and select the best model by comparing the performance scores of multiple models when trained and tested on the K-folds of the dataset.Overall, cross-validation helps to increase the robustness of the model, provides an efficient way of testing it, and reduces the chances of overfitting. This technique is particularly useful when the amount of data is limited, as it allows for the efficient use of the available data. . Response in Sesotho: Cross-validation ke mokhoa o sebelisoang thutong ea mochini ho lekola tsebetso e boletsoeng esale pele ea mohlala, haholo ha o na le data e nyane. E sebelisetsoa ho hakanya hore na mohlala oa ho bolela esale pele o tla sebetsa joang ka nepo. Hangata tsebetso e tsamaea ka tsela e latelang: 1. Ho arola lintlha: Mohato oa pele ke ho arola dataset ka likarolo tse peli: e ngoe bakeng sa ho koetlisa mohlala le e ngoe bakeng sa ho e hlahloba. Leha ho le joalo, ha ho netefatsoa, data e aroloa hape ka mameno a K, moo K e leng palo e boletsoeng esale pele hangata lipakeng tsa 5-10. 2. Koetliso le Teko: Joale, mohlala o koetlisetsoa ho K-1 li-subsets kapa mameno a data le ho tiisetsoa ho karoloana e setseng. Tsebetso ena e phetoa makhetlo a K, ka lemeno le fapaneng le sebelisetsoang ho netefatsa nako le nako. 3. Lekola tšebetso: Tsebetso ea mohlala e khethoa ka karolelano ea lintlha tsa tsebetso ho mameno a K. Sena se fana ka tekanyo e nepahetseng haholoanyane ea hore na mohlala o sebetsa hantle hakae ho data e sa bonahaleng. 4. Khetha Mohlala o motle ka ho fetisisa: Ho netefatsa sefapano ho ka sebelisoa ho bapisa le ho khetha mohlala o motle ka ho fetisisa ka ho bapisa lintlha tsa tsebetso ea mefuta e mengata ha u koetlisitsoe le ho lekoa ho K-folds ea dataset. Ka kakaretso, netefatso ea sefapano e thusa ho eketsa ho tiea ha mohlala, ho fana ka mokhoa o nepahetseng oa ho o leka, le ho fokotsa menyetla ea ho feta. Mokhoa ona o molemo ka ho khetheha ha palo ea data e lekanyelitsoe, kaha e lumella tšebeliso e nepahetseng ea lintlha tse teng. 