<prompt> Bolela mekhoa e meraro e tloaelehileng ea ho netefatsa e sebelisoang Thutong ea Mochini.nan  <response>: Instruction in English: Name three common validation techniques used in Machine Learning. . Response in English: 1. Cross-Validation: Cross-validation is one of the most commonly used validation techniques in machine learning, where the training dataset is divided into several smaller subsets, and the algorithm is trained iteratively, leaving one subset out for validation purposes while training on the remaining subsets. This process is repeated until all subsets have been used as a validation dataset, thus providing a more accurate estimate of how well the model has learned.2. Holdout Method: The holdout method is another widely used validation technique in which the training data is split into two parts, with one part reserved for training the model while the other is used to test the models performance. This technique is often used in the classification problems where the dataset is large and the algorithm is computationally intensive.3. Leave-One-Out Cross-Validation LOOCV: Leave-One-Out Cross-Validation LOOCV is a special case of k-fold cross-validation, where k is equal to the number of samples in the dataset. In this method, the model is trained on all the samples except one which is left out for validation, and the process is repeated for each sample in the dataset. Though this method can be computationally expensive, it provides a near-unbiased estimate of the models performance. . Response in Sesotho: 1. Tlhahiso e Feletseng: Ho netefatsoa ka ho fapanyetsana ke e ngoe ea mekhoa e sebelisoang haholo ea ho netefatsa thutong ea mochini, moo dataset ea koetliso e arotsoeng ka likaroloana tse nyane, me algorithm e koetlisoa khafetsa, e siea karolo e le ngoe hore e netefatsoe. merero ha u ntse u koetlisa likaroloana tse setseng. Tsebetso ena e phetoa ho fihlela li-subsets tsohle li se li sebelisitsoe e le dataset ea netefatso, kahoo e fana ka khakanyo e nepahetseng haholoanyane ea hore na mohlala o ithutile hantle hakae. 2. Mokhoa oa ho tsoara: Mokhoa oa ho tsoara ke mokhoa o mong oa netefatso o sebelisoang haholo moo data ea koetliso e arotsoeng likarolo tse peli, karolo e le ngoe e boloketsoe ho koetlisa mohlala ha e ngoe e sebelisoa ho leka tsebetso ea mohlala. Mokhoa ona o atisa ho sebelisoa mathateng a lihlopha moo dataset e kholo le algorithm e matla haholo. 3. Leave-One-Out Cross-Validation LOOCV: Leave-One-Out Cross-Validation LOOCV ke boemo bo ikgethileng ba k-fold cross-validation, moo k e lekanang le palo ya disampole. ka hara dataset. Ka mokhoa ona, mohlala o koetlisetsoa lisampole tsohle ntle le e le ngoe e siiloeng bakeng sa ho netefatsoa, me tsebetso e phetoa bakeng sa sampole ka ngoe ho dataset. Le hoja mokhoa ona o ka bitsa chelete e ngata, o fana ka khakanyo e batlang e se na leeme ea tšebetso ea mohlala. 