<prompt> Hlalosa hore na hyperparameter optimization ke eng me u thathamise mekhoa e meraro e tsebahalang.nan  <response>: Instruction in English: Describe what is hyperparameter optimization and list three popular techniques. . Response in English: Hyperparameter optimization is the process of selecting appropriate values for hyperparameters, which are variables that determine the structure and behavior of a machine learning model. These hyperparameters define important characteristics of the model such as complexity, capacity, and flexibility. Since the performance of a model can be highly dependent on the values of its hyperparameters, optimizing them can significantly improve the models ability to learn from data.There are several popular techniques for hyperparameter optimization, including:1. Grid search: This technique involves specifying a list of values for each hyperparameter, and subsequently training a model for each combination of hyperparameters. Though this technique is straightforward and easy to implement, it is often computationally expensive, especially as the number of hyperparameters and the number of values for each hyperparameter increases.2. Random search: This technique is similar to grid search, but instead of specifying a list of values for each hyperparameter, values are randomly selected from a predefined distribution for each hyperparameter. Random search is often more efficient than grid search, especially when the number of hyperparameters is large.3. Bayesian optimization: This technique involves constructing a probabilistic model of the function that maps hyperparameters to the models performance, and subsequently using this model to select hyperparameters that are expected to yield a good performance. Bayesian optimization is often more efficient than grid search and random search, as it does not require the evaluation of all possible combinations of hyperparameters. . Response in Sesotho: Hyperparameter optimization ke mokhoa oa ho khetha litekanyetso tse nepahetseng bakeng sa li-hyperparameter, e leng mefuta e fapaneng e khethollang sebopeho le boitšoaro ba mohlala oa ho ithuta mochine. Li-hyperparameter tsena li hlalosa litšobotsi tsa bohlokoa tsa mohlala joalo ka ho rarahana, bokhoni le ho feto-fetoha ha maemo. Kaha tsebetso ea mohlala e ka itšetleha haholo ka boleng ba li-hyperparameter tsa eona, ho li ntlafatsa ho ka ntlafatsa haholo bokhoni ba mohlala ba ho ithuta ho data. Ho na le mekhoa e maloa e tsebahalang ea ho ntlafatsa hyperparameter, ho kenyelletsa: 1. Patlo ea marang-rang: Mokhoa ona o kenyelletsa ho hlakisa lethathamo la boleng bakeng sa hyperparameter ka ngoe, ebe o koetlisa mohlala bakeng sa motsoako o mong le o mong oa hyperparameter. Leha mokhoa ona o otlolohile ebile o le bonolo ho o kenya tsebetsong, hangata o theko e boima haholo, haholo ha palo ea li-hyperparameter le palo ea boleng ba hyperparameter ka ngoe e ntse e eketseha. 2. Patlo e sa reroang: Mokhoa ona o tšoana le oa ho batla marang-rang, empa ho e-na le ho hlakisa lethathamo la boleng bakeng sa hyperparameter ka ngoe, litekanyetso li khethoa ka mokhoa o sa reroang ho tloha kabong e boletsoeng esale pele bakeng sa hyperparameter ka ngoe. Patlo e sa reroang hangata e sebetsa hantle ho feta ho batla marang-rang, haholo ha palo ea li-hyperparameter e le ngata. 3. Koetliso ea Bayesian: Mokhoa ona o kenyelletsa ho aha mohlala oa probabilistic oa tsebetso e bontsang li-hyperparameter ho tsebetso ea mohlala, me ka mora moo e sebelise mokhoa ona ho khetha li-hyperparameter tse lebelletsoeng ho hlahisa tsebetso e ntle. Ntlafatso ea Bayesian hangata e sebetsa hantle ho feta ho batla marang-rang le ho batla ka tšohanyetso, kaha ha e hloke tlhahlobo ea mefuta eohle e ka bang teng ea li-hyperparameter. 