<prompt> Hlalosa litšoaneleho tsa mehlala ea lihlahisoanan  <response>: Instruction in English: Explain the characteristics of generative models . Response in English: Generative models are a type of machine learning model that attempt to generate new data that are similar to a given dataset. They operate by learning the underlying system or process by which data is produced, and then using this information to generate new data that is consistent with the statistical properties of the input dataset.Some of the characteristic features of generative models include:- They capture the joint probability distribution of the input data: This means that generative models can represent the various relationships and dependencies among different features in the data.- They allow for the generation of new data: One of the most important features of generative models is that they can be used to generate new, synthetic data that looks similar to the original data. This is useful for a variety of applications, including data augmentation and image or text generation.- They can handle missing data: Because generative models capture the joint distribution of the data, they are able to handle missing data by estimating the most likely value of the missing data based on the given information.- They can be used for tasks such as classification: Generative models can also be used for tasks such as classification by modeling the probability that an instance belongs to a particular class based on the features of the data.- They can be more computationally intensive: Compared to other models such as discriminative models, generative models are often more computationally intensive to train, because they require estimating the joint distribution of the data. However, once trained, they can be quite efficient at generating new data.Examples of generative models include Restricted Boltzmann Machines, Variational Autoencoders VAEs, and Generative Adversarial Networks GANs. . Response in Sesotho: Mefuta e hlahisoang ke mofuta oa mochini oa ho ithuta o lekang ho hlahisa data e ncha e tsoanang le dataset e fanoeng. Ba sebetsa ka ho ithuta tsamaiso ea motheo kapa mokhoa oo boitsebiso bo hlahisoang ka oona, ebe ba sebelisa boitsebiso bona ho hlahisa lintlha tse ncha tse lumellanang le litšobotsi tsa lipalo-palo tsa dataset ea ho kenya. Likarolo tse ling tsa mefuta ea tlhahiso li kenyelletsa: - Ba hapa monyetla oa ho ajoa ha data e kentsoeng: Sena se bolela hore mefuta ea tlhahiso e ka emela likamano tse fapaneng le tse itsetlehang lipakeng tsa likarolo tse fapaneng tsa data. - Li lumella ho hlahisa lintlha tse ncha: E ngoe ea likarolo tsa bohlokoa ka ho fetisisa tsa mehlala ea lihlahisoa ke hore li ka sebelisoa ho hlahisa lintlha tse ncha, tsa maiketsetso tse shebahalang li tšoana le data ea pele. Sena se na le thuso bakeng sa litsebetso tse fapaneng, ho kenyeletsoa ntlafatso ea data le tlhahiso ea litšoantšo kapa mongolo. - Ba khona ho sebetsana le lintlha tse sieo: Hobane mefuta ea lihlahisoa e tšoara kabo e kopanetsoeng ea data, e khona ho sebetsana le data e sieo ka ho lekanya boleng bo ka bang teng ba data e sieo ho latela lintlha tse fanoeng. - Li ka sebelisoa bakeng sa mesebetsi e kang ho arola: Mehlala ea tlhahiso e ka boela ea sebelisoa bakeng sa mesebetsi e kang ho arola ka ho etsa mohlala oa monyetla oa hore mohlala ke oa sehlopha se itseng ho latela likarolo tsa data. - Li ka ba tse matla haholoanyane: Ha li bapisoa le mefuta e meng e kang mefuta e khethollang, mefuta ea lihlahisoa hangata e matla haholo ho koetlisa, hobane e hloka ho hakanya kabo e kopanetsoeng ea data. Leha ho le joalo, ha ba se ba koetlisitsoe, ba ka sebetsa hantle haholo ho hlahisa data e ncha. Mehlala ea mefuta ea tlhahiso e kenyelletsa Mechini ea Boltzmann e Restricted, Variational Autoencoder VAEs, le Generative Adversarial Networks GANs. 