<prompt> Ke mefuta efe ea li-algorithms tsa ho ithuta tse hlokometsoeng?nan  <response>: Instruction in English: What are the types of supervised learning algorithms? . Response in English: Supervised learning algorithms can be broadly divided into two types: regression and classification. These algorithms can be further categorized based on their approach and model architecture. Here is a list of some of the most popular supervised learning algorithms:1. Linear Regression: A statistical method for modeling the linear relationship between a dependent variable and one or more independent variables.2. Logistic Regression: A type of regression analysis used for predicting the probability of an event occurring.3. Decision Trees: A non-parametric machine learning method used for classification and regression.4. Naïve Bayes: A classification technique based on Bayes theorem, which assumes the independence of features.5. k-Nearest Neighbors k-NN: A non-parametric method used for both classification and regression, based on the principle of similarity.6. Random Forest: An ensemble learning method that combines multiple decision trees for improved accuracy and reduced overfitting.7. Support Vector Machines SVM: A binary linear classification technique that seeks to find the best boundary between two classes.8. Neural Networks: A type of machine learning algorithm modeled after the structure and function of the human brain, used for classification, regression, and many other tasks.Each of these algorithms has its own set of strengths, weaknesses, and assumptions, making them suitable for different types of problems. Choosing the best algorithm for a specific task may require trying out different methods and comparing their performance. . Response in Sesotho: Litaolo tsa ho ithuta tse hlokometsoeng li ka aroloa ka bophara ka mefuta e meli: ho khutlela morao le ho arola. Li-algorithms tsena li ka aroloa ho latela mokhoa oa bona le meralo ea bona. Mona ke lethathamo la tse ling tsa mekhoa ea ho ithuta e hlokometsoeng ka ho fetisisa e tsebahalang: 1. Linear Regression: Mokhoa oa lipalo-palo oa ho etsa mohlala oa kamano ea linear pakeng tsa phapang e itšetlehileng le e le ngoe kapa tse ngata tse ikemetseng. 2. Phatlalatso ea Lintho: Mofuta oa tlhahlobo ea ho khutlela morao o sebelisetsoang ho bolela esale pele monyetla oa hore ketsahalo e etsahale. 3. Lifate tsa Liqeto: Mokhoa oa ho ithuta oa mochine o se nang parametric o sebelisetsoang ho arola le ho khutlela morao. 4. Naïve Bayes: Mokhoa oa ho hlophisa o ipapisitse le theorem ea Bayes, e nkang boikemelo ba likarolo. 5. k-Baahelani ba Haufi ka ho Fetisisa k-NN: Mokhoa o seng oa parametric o sebelisoang bakeng sa ho arola le ho fokotseha, ho itšetlehile ka molao-motheo oa ho tšoana. 6. Random Forest: Mokhoa oa ho ithuta o kopaneng o kopanyang lifate tse ngata tsa liqeto bakeng sa ho nepahala ho ntlafetseng le ho fokotsa ho feta tekano. 7. Mechini ea Tsehetso ea Vector SVM: Mokhoa oa ho khetholla li-binary linear o batlang ho fumana moeli o motle ka ho fetisisa pakeng tsa lihlopha tse peli. 8. Neural Networks: Mofuta oa algorithm ea ho ithuta mochini o hlophisitsoeng ka mora sebopeho le tsebetso ea boko ba motho, e sebelisetsoang ho arola, ho khutlela morao, le mesebetsi e meng e mengata. E ngoe le e ngoe ea li-algorithms tsena e na le matla, bofokoli le mehopolo ea eona, e etsang hore e tšoanele mefuta e fapaneng ea mathata. Ho khetha algorithm e ntle ka ho fetisisa bakeng sa mosebetsi o itseng ho ka hloka ho leka mekhoa e fapaneng le ho bapisa tsebetso ea bona. 