<prompt> Ke mekhoa efe e ka sebelisoang ho ntlafatsa ho nepahala ha mefuta ea ho ithuta ka mochini?nan  <response>: Instruction in English: What methods can be used to improve the accuracy of machine learning models? . Response in English: Here are several methods to improve the accuracy of machine learning models:1. Gathering and cleaning more data: The more data the model is trained on, the more patterns it can recognize, and the better it can generalize to new data. Its also important to clean the data to handle missing, inconsistent or duplicate information.2. Data preprocessing: Several preprocessing techniques like normalization, standardization, encoding, and feature selection can improve model accuracy by making the data more suitable for the learning algorithm.3. Selecting the right algorithm: Different algorithms are suited to different types of data and problems. Experimenting with various algorithms, including ensemble methods that combine several algorithms, can boost accuracy.4. Hyperparameter tuning: Hyperparameters are the fixed parameters that control the behavior of the learning algorithm. Tuning them can sometimes result in large improvements in accuracy.5. Regularization: Regularization is a technique to prevent overfitting by adding a penalty term to the loss function. It encourages the model to use all the input features in a more balanced way, thus resulting in better generalization on new data.6. Cross-validation: Cross-validation is a technique to evaluate how well the model generalizes on new, unseen data. By using cross-validation to help choose the best combination of preprocessing techniques, algorithms, and hyperparameters, its possible to increase model accuracy. . Response in Sesotho: Mona ke mekhoa e mengata ea ho ntlafatsa ho nepahala ha mefuta ea ho ithuta ka mochini: 1. Ho bokella le ho hloekisa lintlha tse ngata: Ha mohlala o koetlisoa ka boitsebiso bo bongata, o ka lemoha mekhoa e mengata, me e ka ntlafatsa haholoanyane ho data e ncha. Ho bohlokoa hape ho hloekisa data ho sebetsana le litaba tse sieo, tse sa lumellaneng kapa tse kopitsoang. 2. Tsebetso ea data esale pele: Mekhoa e mengata ea ho lokisa esale pele joalo ka tloaelo, maemo, khouto, le khetho ea likarolo e ka ntlafatsa ho nepahala ha mohlala ka ho etsa hore data e lokele algorithm ea ho ithuta. 3. Ho khetha algorithm e nepahetseng: Li-algorithms tse fapaneng li loketse mefuta e fapaneng ea data le mathata. Ho etsa liteko ka li-algorithms tse fapaneng, ho kenyelletsa le mekhoa e kopaneng e kopanyang li-algorithms tse maloa, ho ka matlafatsa ho nepahala. 4. Hyperparameter tuning: Li-hyperparameter ke li-parameter tse tsitsitseng tse laolang boitšoaro ba algorithm ea ho ithuta. Ho li lokisa ka linako tse ling ho ka fella ka ntlafatso e kholo ea ho nepahala. 5. Regularization: Regularization ke mokhoa oa ho thibela ho feta tekano ka ho eketsa nako ea kotlo mosebetsing oa tahlehelo. E khothalletsa mohlala ho sebelisa likarolo tsohle tsa ho kenya ka mokhoa o leka-lekaneng, ka hona ho fella ka kakaretso e ntlafetseng ho data e ncha. 6. Phatlalatso e fapaneng: netefatso ea sefapano ke mokhoa oa ho lekola hore na mohlala o akaretsa hantle hakae ho data e ncha, e sa bonahaleng. Ka ho sebelisa netefatso ea sefapano ho thusa ho khetha motsoako o motle oa mekhoa ea ho lokisa esale pele, li-algorithms le li-hyperparameter, hoa khoneha ho eketsa ho nepahala ha mohlala. 