<prompt> Hlalosa mohopolo oa ho arola lileibole tse ngata.nan  <response>: Instruction in English: Explain the concept of multi-label classification. . Response in English: Multi-label classification refers to a type of supervised machine learning problem where the goal is to predict a set of multiple labels for a given instance. It differs from binary or multiclass classification in that binary classification deals with predicting one of two categories, while multiclass classification predicts one of multiple categories, and multi-label classification predicts multiple labels for each instance.An example of multi-label classification problem is a movie recommendation system where each movie can be assigned multiple genres such as comedy, action, and romance simultaneously. Another example is text classification, where a document could be classified into multiple topics or categories.To solve multi-label classification problems, different approaches can be taken. One common approach is to use binary classifiers for each label and combine their results. Another approach is to use specific algorithms designed for multi-label classification problems such as Random Forest, K-nearest neighbors, and artificial neural networks.In summary, multi-label classification involves predicting multiple outcomes or labels for a given instance using supervised machine learning algorithms. It is commonly used in scenarios where there are multiple overlapping outcomes or classes. . Response in Sesotho: Khethollo ea lileibole tse ngata e bua ka mofuta oa bothata ba ho ithuta ka mochini o hlokometsoeng moo sepheo e leng ho bolela esale pele sehlopha sa lileibole tse ngata ketsahalong e itseng. E fapane le lihlopha tsa binary kapa tse ngata ka hore lihlopha tsa binary li sebetsana le ho bolela esale pele e ngoe ea mekhahlelo e meli, ha lihlopha tse ngata li bolela esale pele e ngoe ea lihlopha tse ngata, me lihlopha tse ngata li bolela esale pele li-labels tse ngata bakeng sa ketsahalo ka ngoe. Mohlala oa bothata ba ho arola li-label tse ngata ke sistimi ea khothaletso ea lifilimi moo filimi ka ngoe e ka abeloang mefuta e mengata joalo ka metlae, ketso, le marato ka nako e le ngoe. Mohlala o mong ke oa ho aroloa ha mongolo, moo tokomane e ka aroloang ka lihlooho kapa mekhahlelo e mengata. Ho rarolla mathata a mefuta e mengata ea li-label, ho ka etsoa mekhoa e fapaneng. Mokhoa o mong o tloaelehileng ke ho sebelisa li-classifiers tsa binary bakeng sa label ka ngoe le ho kopanya liphetho tsa bona. Mokhoa o mong ke oa ho sebelisa li-algorithms tse ikhethileng tse etselitsoeng mathata a ho arola li-label tse ngata joalo ka Random Forest, baahisani ba haufi ba K, le marang-rang a maiketsetso a methapo ea kutlo. Ka bokhutsoanyane, ho arola lileibole tse ngata ho kenyelletsa ho bolela esale pele liphetho tse ngata kapa lileibole bakeng sa ketsahalo e itseng ho sebelisoa li-algorithms tsa ho ithuta ka mochini. E sebelisoa hangata maemong ao ho ona ho nang le liphetho kapa lihlopha tse fapaneng tse fapaneng. 