<prompt> Thathamisa mekhoa e 4 ea ho sebetsana le thohako ea boholo.nan  <response>: Instruction in English: List 4 methods for dealing with the curse of dimensionality. . Response in English: The curse of dimensionality refers to the problems that arise when working with high-dimensional data sets, where the number of features or dimensions is very large in comparison to the number of data points. There are several methods that can be used to deal with the curse of dimensionality, including:1. Dimensionality Reduction: This involves reducing the number of dimensions or features of the data set to a more manageable number. Common techniques include Principal Component Analysis PCA and Linear Discriminant Analysis LDA.2. Feature Selection: This involves identifying the most important features of the data set and discarding the rest. Various methods can be used, such as correlation analysis, mutual information, and Recursive Feature Elimination RFE.3. Regularization: This involves adding a penalty term to the model to reduce the complexity of the model and prevent overfitting. Popular methods include Lasso and Ridge regression.4. Data Sampling: This involves using a smaller subset of the data to train the model, thereby reducing the dimensionality of the data set. There are various sampling techniques that can be used, including Random Sampling, Stratified Sampling, and Cluster Sampling. . Response in Sesotho: Thohako ea dimensionality e bua ka mathata a hlahang ha ho sebetsa le lisebelisoa tsa data tse phahameng, moo palo ea likarolo kapa litekanyo e leng khōlō haholo ha e bapisoa le palo ea lintlha tsa data. Ho na le mekhoa e maloa e ka sebelisoang ho sebetsana le thohako ea boholo, ho kenyelletsa: 1. Phokotso ea Bophahamo: Sena se kenyelletsa ho fokotsa palo ea litekanyo kapa likarolo tsa data e behiloeng ho nomoro e laolehang haholoanyane. Mekhoa e tloaelehileng e kenyelletsa Principal Component Analysis PCA le Linear Discriminant Analysis LDA. 2. Khetho ea Likarolo: Sena se kenyelletsa ho khetholla likarolo tsa bohlokoahali tsa sete ea data le ho lahla tse ling kaofela. Mekhoa e fapaneng e ka sebelisoa, joalo ka tlhahlobo ea khokahano, tlhaiso-leseling, le Recursive Feature Elimination RFE. 3. Regularization: Sena se kenyelletsa ho eketsa nako ea kotlo ho mohlala ho fokotsa ho rarahana ha mohlala le ho thibela ho feta. Mekhoa e tsebahalang e kenyelletsa Lasso le Ridge regression. 4. Data Sampling: Sena se kenyelletsa ho sebelisa karoloana e nyane ea data ho koetlisa mohlala, ka hona ho fokotsa boholo ba sete ea data. Ho na le mekhoa e fapaneng ea lisampole e ka sebelisoang, ho kenyeletsoa Random Sampling, Stratified Sampling, le Cluster Sampling. 