<prompt> Etsa mohlala bakeng sa mosebetsi o fanoeng.Mosebetsi oa Sehlopha  <response>: Instruction in English: Generate an example for the given task. . Response in English: Example: Email Spam DetectionEmail Spam Detection is a binary classification task that seeks to determine whether an email message is spam or not spam. This classification task can analyze various features such as the email sender, subject, content, and even metadata. The goal is to classify emails to prevent spam emails from reaching the inbox and to ensure that important emails are not misidentified as spam. Machine learning algorithms like Naive Bayes, Support Vector Machines SVM, and Decision Trees are commonly used for this task. The model is trained on a dataset of pre-labeled emails, where the algorithm learns to identify patterns and features associated with the spam or not spam classes. Once the model is trained, it can be used to classify new incoming emails, automatically filtering out spam messages to the spam folder. . Response in Sesotho: Mohlala: Tlhahlobo ea lengolo-tsoibila la lengolo-tsoibila la Spam ke mosebetsi oa ho khetholla ka mokhoa o ikhethileng o batlang ho tseba hore na molaetsa oa lengolo-tsoibila ke spam kapa che. Mosebetsi ona oa ho hlophisa o ka sekaseka likarolo tse fapaneng joalo ka moromeli oa lengolo-tsoibila, sehlooho, litaba, esita le metadata. Sepheo ke ho hlophisa li-imeile ho thibela li-imeile tsa spam ho kena ka hara inbox le ho netefatsa hore li-imeile tsa bohlokoa ha li tsejoe hampe e le spam. Mekhoa ea ho ithuta ka mochini e kang Naive Bayes, Support Vector Machines SVM, le Decision Trees hangata e sebelisoa bakeng sa mosebetsi ona. Moetso ona o koetliselitsoe lethathamong la li-imeile tse ngoliloeng pele, moo algorithm e ithutang ho tseba mekhoa le likarolo tse amanang le litlelase tsa spam kapa eseng tsa spam. Hang ha mohlala o se o koetliselitsoe, o ka sebelisoa ho hlophisa li-imeile tse ncha tse kenang, ka ho iketsetsa melaetsa ea spam ho fensetere ea spam. 