<prompt> Phapano ke efe lipakeng tsa mekhoa ea ho ithuta e hlokometsoeng le e sa behoang leihlo?nan  <response>: Instruction in English: What are the differences between supervised and unsupervised learning algorithms? . Response in English: At a high level, the main difference between supervised and unsupervised learning algorithms is how they handle the relationship between the input features and the output or desired results.In supervised learning, algorithms use labeled training data to learn the relationship between the input features and the output variable. This means that the data already includes the known results or desired answers and the algorithm is trained to to find a prediction model that can accurately map the new input to its corresponding output. Supervised learning can be further divided into regression when the output is continuous and classification when the output is categorical tasks. Examples of supervised learning algorithms include logistic regression, decision trees, and support vector machines. In contrast, unsupervised learning algorithms do not use labeled training data, and instead, they try to find patterns or structure in the data without being explicitly told what the desired output should be. The algorithms strive to learn the underlying relationships between the data points without any external guidance. This type of learning is typically used for exploratory purposes, such as when attempting to find clusters or patterns in data, or for dimensionality reduction. Common unsupervised learning techniques include k-means clustering, hierarchical clustering, and principal component analysis.In summary, the main difference between supervised and unsupervised learning is the presence or absence of labeled training data. While supervised algorithms use the known results to learn a prediction model, unsupervised algorithms are tasked with finding structure in the data on their own. . Response in Sesotho: Boemong bo phahameng, phapang e ka sehloohong pakeng tsa mekhoa ea ho ithuta e hlokometsoeng le e sa behoang leihlo ke kamoo ba sebetsanang le kamano pakeng tsa likarolo tsa ho kenya letsoho le sephetho kapa liphetho tse lakatsehang. Thutong e hlokometsoeng, li-algorithms li sebelisa lintlha tsa koetliso tse ngotsoeng ho ithuta kamano pakeng tsa likarolo tsa ho kenya letsoho le phapano ea tlhahiso. Sena se bolela hore data e se e ntse e kenyelletsa liphetho tse tsejoang kapa likarabo tse lakatsehang mme algorithm e koetliselitsoe ho fumana mohlala oa ho bolela esale pele o ka mapa ka nepo tlhahiso e ncha ho tlhahiso ea eona e lumellanang. Thuto e hlokometsoeng e ka aroloa hape ka ho khutlela morao ha sephetho se tsoela pele le karohano ha tlhahiso e le ka lihlopha mesebetsi. Mehlala ea li-algorithms tsa ho ithuta tse hlokometsoeng li kenyelletsa ho fokotseha ha lintho, lifate tsa liqeto, le mechini ea tšehetso ea li-vector. Ka lehlakoreng le leng, li-algorithms tsa ho ithuta tse sa laoleheng ha li sebelise lintlha tsa koetliso tse ngotsoeng, me ho e-na le hoo, li leka ho fumana mekhoa kapa sebopeho ho data ntle le ho bolelloa ka ho hlaka hore na tlhahiso e lakatsehang e lokela ho ba efe. Li-algorithms li leka ho ithuta likamano tse teng lipakeng tsa lintlha tsa data ntle le tataiso ea kantle. Mofuta ona oa ho ithuta hangata o sebelisoa molemong oa boithuto, joalo ka ha o leka ho fumana lihlopha kapa lipaterone ho data, kapa ho fokotsa boholo. Mekhoa e tloaelehileng ea ho ithuta e sa laoleheng e kenyelletsa k-means clustering, sehlopha sa maemo a holimo, le tlhahlobo ea likarolo tsa mantlha. Ka bokhutsoane, phapang e kholo lipakeng tsa thuto e hlokometsoeng le e sa laoleheng ke boteng kapa ho ba sieo ha lintlha tsa koetliso tse ngotsoeng. Le hoja li-algorithms tse hlokometsoeng li sebelisa liphetho tse tsejoang ho ithuta mohlala oa ho bolela esale pele, li-algorithms tse sa laoleheng li filoe mosebetsi oa ho fumana sebopeho ho data ka botsona. 