<prompt> Bolela mekhoa e 5 ea khetho ea likarolo.nan  <response>: Instruction in English: Name 5 methods of feature selection. . Response in English: 1. Filter methods: These methods select features based on their correlation with the target variable or other statistical measures such as variance, chi-squared and mutual information. 2. Wrapper methods: These methods evaluate the performance of different subsets of features using cross-validation, and select the subset with the best performance. Some examples of wrapper methods include forward selection, backward elimination, and recursive feature elimination. 3. Embedded methods: These methods incorporate feature selection as a part of the model building process. Some examples include LASSO, ridge regression, and decision trees. 4. Regularization: This method adds a penalty term to the cost function in order to reduce the complexity of the model and prevent over-fitting. This results in shrinking the coefficients of the less important features to zero, thus removing them from the model. 5. Dimensionality reduction techniques: These methods are used to reduce the number of features by creating new features that represent the data as effectively as the original features. Some common techniques include Principal Component Analysis, Linear Discriminant Analysis and Non-Negative Matrix Factorisation. . Response in Sesotho: 1. Mekhoa ea ho sefa: Mekhoa ena e khetha likarolo tse thehiloeng kamanong ea tsona le sepheo sa ho feto-fetoha kapa mehato e meng ea lipalo-palo e kang phapang, chi-squared le tlhahisoleseding e kopanetsoeng. 2. Mekhoa ea ho phuthela: Mekhoa ena e lekola tsebetso ea likaroloana tse fapaneng tsa likarolo li sebelisa netefatso e fapaneng, me u khethe karoloana e nang le tsebetso e ntle ka ho fetisisa. Mehlala e meng ea mekhoa ea wrapper e kenyelletsa khetho ea pele, ho felisoa ka morao, le ho felisoa ha likarolo tse iphetang. 3. Mekhoa e kenyelletsoeng: Mekhoa ena e kenyelletsa khetho ea likarolo e le karolo ea mokhoa oa ho haha mohlala. Mehlala e meng e kenyelletsa LASSO, ridge regression, le lifate tsa liqeto. 4. Regularization: Mokhoa ona o eketsa nako ea kotlo mosebetsing oa litšenyehelo e le ho fokotsa ho rarahana ha mohlala le ho thibela ho feta tekano. Sena se fella ka ho fokotsa li-coefficients tsa likarolo tse seng bohlokoa haholo ho zero, kahoo li li tlosa ho tloha mohlala. 5. Mekhoa ea ho fokotsa boholo: Mekhoa ena e sebelisetsoa ho fokotsa palo ea likarolo ka ho theha likarolo tse ncha tse emelang lintlha ka katleho joaloka likarolo tsa pele. Mekhoa e meng e tloaelehileng e kenyelletsa Tlhahlobo ea Karolo e ka Sehloohong, Tlhahlobo ea Khethollo ea Linear le Non-Negative Matrix Factorisation. 