<prompt> Ke li-algorithms life tse peli tse atileng haholo tsa hyperparameter tuning tse sebelisoang thutong ea mochini?nan  <response>: Instruction in English: What are the two most common hyperparameter tuning algorithms used in machine learning? . Response in English: The two most common hyperparameter tuning algorithms used in machine learning are Grid Search and Random Search. - Grid Search: This method involves creating a grid of hyperparameter values and trying all the possible combinations to find the model that gives the best output. This can be computationally expensive, especially when working with a large number of hyperparameters and high-dimensional data.- Random Search: This method randomly samples the hyperparameter values from a defined search space, then tests these combinations for their model performance. This approach can be less computationally expensive than grid search and can sometimes find a better combination of hyperparameters than grid search by pure chance. . Response in Sesotho: Li-algorithms tse peli tse atileng haholo tsa tokiso ea hyperparameter tse sebelisoang ho ithuteng ka mochini ke Patlo ea Grid le Patlo e sa Lebelloang. - Patlo ea Grid: Mokhoa ona o kenyelletsa ho theha grid ea boleng ba hyperparameter le ho leka metsoako eohle e ka khonehang ho fumana mofuta o fanang ka tlhahiso e ntle ka ho fetisisa. Sena se ka ba theko e boima haholo, haholo ha o sebetsa le palo e kholo ea li-hyperparameter le data ea boemo bo holimo. - Patlisiso e sa reroang: Mokhoa ona o etsa sampole ea boleng ba hyperparameter ka mokhoa o sa reroang sebakeng se hlalositsoeng sa patlo, ebe o leka metsoako ena bakeng sa tsebetso ea bona ea mohlala. Mokhoa ona o ka ba theko e tlase ha o bapisoa le ho batla marang-rang mme ka linako tse ling o ka fumana motsoako o betere oa hyperparameter ho feta ho batla marang-rang ka monyetla o hlakileng. 