<prompt> Bapisa le ho bapisa mefuta e meli ea thuto ea mochini e hlokometsoeng.nan  <response>: Instruction in English: Compare and contrast the two types of supervised machine learning. . Response in English: Supervised machine learning is a subset of machine learning where the model is trained on a labeled dataset to make predictions or decisions, without being explicitly programmed to do so. This type of machine learning possesses two main types: regression and classification.Regression analysis is a type of supervised machine learning algorithm used for predicting a continuous outcome variable a dependent variable based on the value of one or several independent variables. For example, predicting an individuals salary based on factors such as years of experience, age, gender, and race. Regression algorithms attempt to model the relationship between the independent and dependent variables in order to understand how the dependent variable changes as the independent variables change.On the other hand, classification is another type of supervised machine learning algorithm that involves the generation of a model that separates data into classes or categories. This algorithm is used with discrete or categorical dependent variables. For example, predicting whether an email is spam or not spam. In this case, the algorithm must determine the likelihood of an email being spam or not spam based on the specific content and context of the email such as keywords, phrases or other features.In summary, the key difference between regression and classification is that regression focuses on predicting continuous values while classification focuses on predicting discrete or categorical values. Furthermore, while both types of algorithms are used for prediction, the way they make predictions is also different; regression predicts values by modeling the relationship between the dependent variable and the independent variable, whereas classification separates data into classes or categories. . Response in Sesotho: Ho ithuta ka mochini o hlokometsoeng ke karolo e nyane ea ho ithuta ka mochini moo moetso o koetlisetsoang pokellong ea data e ngolisitsoeng ho etsa likhakanyo kapa liqeto, ntle le ho hlophisoa ka ho hlaka ho etsa joalo. Mofuta ona oa ho ithuta ka mochini o na le mefuta e meli ea mantlha: ho fokotseha le ho hlopha. Tlhahlobo ea ho khutlela morao ke mofuta oa algorithm ea ho ithuta mochine o hlokometsoeng o sebelisetsoang ho bolela esale pele phetoho e tsoelang pele ea liphello mofuta o itšetlehileng ka eona ho latela boleng ba mefuta e le ngoe kapa tse maloa tse ikemetseng. Mohlala, ho bolela esale pele moputso oa motho ho ipapisitsoe le lintlha tse kang lilemo tsa boiphihlelo, lilemo, bong le morabe. Li-algorithms tsa ho khutlela morao li leka ho etsa mohlala oa kamano pakeng tsa mefuta e ikemetseng le e itšetlehileng ka eona e le ho utloisisa hore na phetoho e itšetlehileng ka eona e fetoha joang ha mefuta e ikemetseng e fetoha. Ka lehlakoreng le leng, ho hlophisoa ke mofuta o mong oa algorithm ea ho ithuta mochini o hlokometsoeng o kenyelletsang tlhahiso ea mohlala o arolang data ka lihlopha kapa mekhahlelo. Algorithm ena e sebelisoa ka mefuta e itšetlehileng ka discrete kapa karohano. Mohlala, ho bolela esale pele hore na lengolo-tsoibila ke spam kapa che. Tabeng ena, algorithm e tlameha ho fumana monyetla oa hore lengolo-tsoibila e be spam kapa e se spam ho latela litaba le maemo a lengolo-tsoibila joalo ka mantsoe a bohlokoa, lipoleloana kapa likarolo tse ling. Ka bokhutsoane, phapang e ka sehloohong lipakeng tsa ho hatelloa le ho hlopha ke hore ho kheloha ho shebana le ho bolela boleng bo tsoelang pele ha karohano e tsepamisitse maikutlo ho boneng esale pele boleng bo sa tsitsang kapa ka mekhahlelo. Ho feta moo, le hoja mefuta e meli ea li-algorithms e sebelisetsoa ho bolela esale pele, tsela eo ba etsang likhakanyo ka eona e boetse e fapane; ho fokotseha ho bolela esale pele boleng ka ho etsa mohlala oa kamano pakeng tsa phapang e itšetlehileng ka eona le phapang e ikemetseng, athe ho arola ka lihlopha ho arola lintlha ka lihlopha kapa lihlopha. 