<prompt> Hlalosa mohopolo oa hyperparameter optimizationnan  <response>: Instruction in English: Explain the concept of hyperparameter optimization . Response in English: Hyperparameter optimization is the process of searching for the optimal values of hyperparameters or tuning parameters to improve the performance of a machine learning or artificial intelligence algorithm. These hyperparameters are the external constraints or settings that the algorithm cannot learn from the data, and they can significantly affect the behavior and accuracy of the algorithm. Some common examples of hyperparameters include the learning rate, number of layers, and the number of neurons per layer in a neural network, the penalty in a support vector machine, and the number of clusters in a k-means clustering algorithm, among others.One way to approach the optimization of hyperparameters is to perform a grid search, which exhaustively searches through a manually specified subset of the hyperparameter space, to evaluate the performance of the algorithm for each combination of values. Another approach is to use randomized search, which samples parameter settings at random and tests their performance. A more sophisticated approach is to use Bayesian optimization, in which an algorithm models the performance of the machine learning algorithm as a function of its hyperparameters and iteratively chooses the next hyperparameters to test based on the models estimates.Ultimately, the goal of hyperparameter optimization is to find a set of values for the hyperparameters that leads to the best possible performance of the algorithm on the given problem. This can be a challenging and time-consuming process, but it is essential for obtaining reliable and accurate results from machine learning and artificial intelligence algorithms. . Response in Sesotho: Hyperparameter optimization ke mokhoa oa ho batla boleng bo nepahetseng ba li-hyperparameter kapa li-parameter tsa tokiso ho ntlafatsa tsebetso ea ho ithuta ka mochini kapa algorithm ea bohlale ba maiketsetso. Li-hyperparameter tsena ke litšitiso kapa litlhophiso tsa kantle tseo algorithm e ke keng ea ithuta tsona ho data, me li ka ama haholo boitšoaro le ho nepahala ha algorithm. Mehlala e meng e tloahelehileng ea li-hyperparameter e kenyelletsa sekhahla sa ho ithuta, palo ea li-layers, le palo ea li-neuron ka lera ho netweke ea methapo, kotlo mochining oa vector ea tšehetso, le palo ea lihlopha ka hara algorithm ea ho kopanya k-ho bolela, hara tse ling. Tsela e ngoe ea ho atamela optimization ea hyperparameters ke ho etsa lipatlisiso tsa marang-rang, tse batlisisang ka botlalo ka hara karolo e boletsoeng ka letsoho ea sebaka sa hyperparameter, ho lekola tsebetso ea algorithm bakeng sa motsoako o mong le o mong oa boleng. Mokhoa o mong ke oa ho sebelisa lipatlisiso tse sa reroang, tse lekanyang litlhophiso tsa parameter ka tšohanyetso le ho lekola tsebetso ea tsona. Mokhoa o rarahaneng le ho feta ke oa ho sebelisa Bayesian optimization, moo algorithm e bontsang tsebetso ea algorithm ea ho ithuta mochini joalo ka tsebetso ea li-hyperparameter tsa eona mme e khetha khafetsa li-hyperparameter tse latelang ho leka ho latela likhakanyo tsa mohlala. Qetellong, sepheo sa hyperparameter optimization ke ho fumana sehlopha sa litekanyetso bakeng sa li-hyperparameter tse lebisang tsebetsong e ntle ka ho fetisisa ea algorithm bothateng bo fanoeng. Sena e ka ba tsebetso e thata ebile e nka nako, empa e bohlokoa bakeng sa ho fumana liphetho tse tšepahalang le tse nepahetseng ho tsoa ho thuto ea mochini le li-algorithms tsa bohlale ba maiketsetso. 