<prompt> Thathamisa mekhoa e 3 ea ho netefatsa mohlala oa ho ithuta oa mochininan  <response>: Instruction in English: List 3 methods to validate a machine learning model . Response in English: There are several methods that can be used to validate a machine learning model to assess its performance, and some of them include:1. Holdout Method: This is one of the most common methods, and it involves splitting the dataset into two parts - training and testing. The model is trained on the training set and then evaluated on the testing set. The performance metrics obtained give an estimate of how well the model is likely to perform on unseen data.2. K-fold Cross Validation: This method involves dividing the dataset into k folds, with one fold being used for testing and the remaining k-1 folds used for training. The process is repeated k times, with each fold being used once for testing. The average performance metric is then calculated to give an overall estimate of the models performance.3. Leave One Out Cross Validation LOOCV: This is a variation of the k-fold cross-validation, where k is set to the number of samples in the dataset. Essentially, the model is trained on all samples except one, which is used for testing. This process is repeated for all samples and the average performance metric is calculated. This method is computationally expensive but can give a reliable estimate of the models performance. . Response in Sesotho: Ho na le mekhoa e maloa e ka sebelisoang ho netefatsa mokhoa oa ho ithuta oa mochine ho hlahloba tsebetso ea oona, me e meng ea eona e kenyelletsa: 1. Holdout Method: Ena ke e ngoe ea mekhoa e tloaelehileng, me e kenyelletsa ho arola dataset likarolong tse peli - koetliso. le teko. Mohlala o koetlisoa ka sete ea koetliso ebe o hlahlojoa ka sete ea tlhahlobo. Melemo ea tsebetso e fumanoeng e fana ka khakanyo ea hore na mohlala o ka sebetsa hantle hakae ho data e sa bonahaleng. 2. K-fold Cross Validation: Mokhoa ona o kenyelletsa ho arola dataset ka mameno a k, me lemeno le le leng le sebelisoa bakeng sa tlhahlobo le mameno a setseng a k-1 a sebelisetsoang koetliso. Tsebetso e phetoa makhetlo a k, me lemeno ka leng le sebelisoa hanngoe bakeng sa tlhahlobo. Kakaretso ea metric ea tsebetso e baloa ho fana ka khakanyo e akaretsang ea tsebetso ea mmotlolo. 3. Leave One Out Cross Validation LOOCV: Ena ke phapang ea k-fold cross-validation, moo k e behiloeng ho palo ea lisampole ho dataset. Ha e le hantle, mohlala o koetlisetsoa lisampole tsohle ntle le e le ngoe, e sebelisetsoang tlhahlobo. Tsebetso ena e phetoa bakeng sa disampole tsohle mme palohare ea metric ea tsebetso e baloa. Mokhoa ona o theko e boima haholo empa o ka fana ka khakanyo e tšepahalang ea tsebetso ea mohlala. 