<prompt> Hlalosa hore na ke hobaneng ha ho ba teng ka tšohanyetso ho le bohlokoa bakeng sa mokhoa oa ho ithuta oa mochine.nan  <response>: Instruction in English: Explain why randomness is important for a machine learning system. . Response in English: Randomness plays an important role in many aspects of machine learning. At its core, it helps to improve the overall performance and robustness of machine learning algorithms. Here are several reasons why randomness is important for a machine learning system:1. Initialization of weights: In many machine learning models, especially neural networks, randomness is used to initialize the weights. This helps to break the symmetry of the model and ensures that the model can learn different features from the training data.2. Sampling and shuffling data: Randomness is used to shuffle the data before splitting it into training, validation and test datasets. This ensures that the data is well-distributed and the model is not biased towards certain types of data. Random sampling is also used to select a subset of data for training or evaluation, which can speed up the learning process.3. Stochastic methods: Many machine learning algorithms use stochastic methods, such as stochastic gradient descent, to optimize the model. These methods introduce randomness during training and can help the model to explore different solutions, avoid getting stuck in local optima, and converge faster.4. Regularization: Randomness is also used in various regularization techniques, such as dropout or data augmentation, to prevent overfitting. These techniques randomly modify the data or the model during training, which helps to increase the generalization of the model and improve its performance on new data.Overall, randomness is an essential component of machine learning and plays a crucial role in improving the performance, robustness, and generalizability of machine learning algorithms. . Response in Sesotho: Ho sa lebelloa ho phetha karolo ea bohlokoa likarolong tse ngata tsa ho ithuta ka mochine. Motheong oa eona, e thusa ho ntlafatsa tsebetso e akaretsang le ho tiea ha li-algorithms tsa ho ithuta mochini. Mona ke mabaka a maloa a hore na ke hobaneng ha ntho e sa tloaelehang e le ea bohlokoa bakeng sa mokhoa oa ho ithuta mochine: 1. Ho qala litekanyo: Mefuteng e mengata ea ho ithuta mochine, haholo-holo marang-rang a neural, ho sebelisoa ka mokhoa o sa tloaelehang ho qala litekanyo. Sena se thusa ho senya symmetry ea mohlala le ho etsa bonnete ba hore mohlala o ka ithuta likarolo tse fapaneng ho tloha boitsebisong ba koetliso. 2. Sampling le shuffling data: Randomness e sebelisetsoa ho hlakanya data pele e e arola ka koetliso, netefatso le li-datasets tsa liteko. Sena se tiisa hore data e abuoa hantle me mohlala ha o leeme ho mefuta e itseng ea data. Sampling e sa reroang e boetse e sebelisoa ho khetha karoloana ea data bakeng sa koetliso kapa tlhahlobo, e ka potlakisang mokhoa oa ho ithuta. 3. Mekhoa ea Stochastic: Li-algorithms tse ngata tsa ho ithuta mochini li sebelisa mekhoa ea stochastic, joalo ka ho theoha ha stochastic gradient, ho ntlafatsa mohlala. Mekhoa ena e hlahisa ntho e sa reroang nakong ea koetliso, me e ka thusa mohlala ho fumana litharollo tse fapaneng, ho qoba ho tsoareha ka mokhoa o nepahetseng oa lehae, le ho kopana kapele. 4. Regularization: Randomness e boetse e sebelisoa mekhoeng e fapaneng ea kamehla, joalo ka ho tlohela sekolo kapa ho eketsa data, ho thibela ho feta. Mekhoa ena e fetola data kapa mohlala ka mokhoa o sa reroang nakong ea koetliso, e thusang ho eketsa kakaretso ea mohlala le ho ntlafatsa tshebetso ea eona ho data e ncha. Ka kakaretso, ho se sebetse ke karolo ea bohlokoa ea ho ithuta ka mochini mme e bapala karolo ea bohlokoa ho ntlafatseng tsebetso, ho tiea, le ho hlaka ha li-algorithms tsa ho ithuta mochini. 