<prompt> Bapisa le ho bapisa mekhoa ea ho ithuta e sa laoleheng le e hlokometsoeng.nan  <response>: Instruction in English: Compare and contrast unsupervised and supervised learning models. . Response in English: Supervised and unsupervised learning models are two of the most widely used machine learning approaches. Both are used to analyze and model large datasets to derive insights and discover patterns. However, they differ in a number of key ways.1. Learning Technique: The main difference between supervised and unsupervised learning models is the technique they use to learn from data. Supervised learning models are trained using labeled data, where the desired outcome is already known. They use this data to learn the relationship between the input and output variables and make predictions based on that. In contrast, unsupervised learning models work with unlabeled data, which means that the algorithm has to search for patterns and relationships in the data without any guidance.2. Data Output: Supervised learning models are used for predictive modeling, where the goal is to predict a specific outcome based on input data. This is possible because the data is labeled, and the model can learn from examples. Unsupervised learning models, on the other hand, are used for descriptive modeling, where the goal is to discover underlying patterns and relationships in the data. These models are able to segment the data into clusters or identify patterns that can help to better understand the data.3. Use Case: Supervised learning models are most commonly used in applications that require prediction or classification, such as predicting stock prices or diagnosing diseases. Unsupervised learning models are most commonly used in applications that require data exploration and analysis, such as discovering customer segments or identifying anomalies in data.In summary, supervised and unsupervised learning models are two different approaches to machine learning that have their own strengths and weaknesses. Supervised learning is useful when the desired outcome is already known, and the goal is to make predictions. Unsupervised learning, on the other hand, is useful when the goal is to discover patterns and relationships in data. . Response in Sesotho: Mehlala ea ho ithuta e hlokometsoeng le e sa laoleheng ke mekhoa e meli e sebelisoang haholo ea ho ithuta ka mochini. Ka bobeli li sebelisetsoa ho sekaseka le ho etsa mohlala oa li-database tse kholo ho fumana lintlha le ho fumana mekhoa. Leha ho le joalo, li fapane ka litsela tse ngata tsa bohlokoa. 1. Mokhoa oa ho Ithuta: Phapang e kholo lipakeng tsa mehlala ea ho ithuta e hlokometsoeng le e sa laoleheng ke mokhoa oo ba o sebelisang ho ithuta ho data. Mehlala ea ho ithuta e hlokometsoeng e koetlisoa ho sebelisoa lintlha tse ngolisitsoeng, moo sephetho se lakatsehang se seng se tsejoa. Ba sebelisa lintlha tsena ho ithuta kamano pakeng tsa mefuta e fapaneng ea ho kenya le ea tlhahiso le ho bolela esale pele ho latela seo. Ka lehlakoreng le leng, mekhoa ea ho ithuta e sa laoleheng e sebetsa ka lintlha tse sa ngolisoang, ho bolelang hore algorithm e tlameha ho batla mekhoa le likamano ho data ntle le tataiso leha e le efe. 2. Sephetho sa Lintlha: Mehlala ea ho ithuta e hlokometsoeng e sebelisoa bakeng sa mohlala oa ho lepa, moo sepheo e leng ho lepa sephetho se itseng ho latela lintlha tse kentsoeng. Sena se ka etsahala hobane data e ngotsoe, me mohlala o ka ithuta mehlaleng. Ka lehlakoreng le leng, mehlala ea ho ithuta e sa laoleheng e sebelisoa bakeng sa mohlala o hlalosang, moo sepheo e leng ho fumana mekhoa le likamano tse teng ho data. Mefuta ena e khona ho arola lintlha ka lihlopha kapa ho tseba mekhoa e ka thusang ho utloisisa lintlha hantle. 3. Taba ea Tšebeliso: Mehlala ea ho ithuta e hlokometsoeng e sebelisoa hangata litsebetsong tse hlokang ho lepa kapa ho aroloa, joalo ka ho bolela esale pele litheko tsa thepa kapa ho hlahloba mafu. Mehlala ea ho ithuta e sa behoang leihlo hangata e sebelisoa litsebetsong tse hlokang ho hlahlojoa le ho hlahlojoa ke data, joalo ka ho fumana likarolo tsa bareki kapa ho fumana liphoso ho data. Ka kakaretso, mekhoa ea ho ithuta e hlokometsoeng le e sa behoang leihlo ke mekhoa e meli e fapaneng ea ho ithuta ka mochine e nang le matla le mefokolo ea eona. Thuto e hlokometsoeng e molemo ha sephetho se lakatsehang se se se tsejoa, me sepheo ke ho bolela esale pele. Ka lehlakoreng le leng, thuto e sa laoleheng e molemo ha sepheo e le ho fumana mekhoa le likamano ho data. 