<prompt> Fana ka mohlala oa mofuta oa binary classificationnan  <response>: Instruction in English: Provide an example of a binary classification model . Response in English: A binary classification model is a type of machine learning algorithm that can be used to predict the outcome of a given observation into one of two categories. A common example of a binary classification model is a logistic regression model used for predicting whether an email is spam or not. This model takes in several features of each email, such as the sender, subject, content, and time, and applies a set of learned weights to these features to output a probability score. If the probability score is above a certain threshold, the email is classified as spam, otherwise, it is classified as not spam. The model is trained on a large dataset of previous emails that have been labeled as spam or not spam. The algorithm learns by adjusting the weights based on the errors made during training so that it can accurately predict the class of new emails. The performance of the model can be evaluated using metrics such as accuracy, precision, recall, and F1 score. . Response in Sesotho: Moetso oa likarolo tsa binary ke mofuta oa algorithm ea ho ithuta mochini o ka sebelisoang ho bolela esale pele sephetho sa tlhokomeliso e fanoeng ho e ngoe ea mekhahlelo e meli. Mohlala o tloaelehileng oa mofuta oa binary classification model ke mokhoa oa ho khutlisa thepa o sebelisoang ho bolela esale pele hore na lengolo-tsoibila ke spam kapa che. Moetso ona o nka likarolo tse maloa tsa lengolo-tsoibila ka leng, joalo ka moromeli, sehlooho, litaba, le nako, mme o sebelisa sete ea boima bo ithutoang ho likarolo tsena ho hlahisa lintlha tse ka bang teng. Haeba lintlha tsa monyetla li kaholimo ho moeli o itseng, lengolo-tsoibila le hlalosoa e le spam, ho seng joalo, e hlalosoa e se spam. Moetso ona o koetliselitsoe lethathamong le leholo la data la li-imeile tse fetileng tse ngotsoeng e le spam kapa eseng spam. Algorithm e ithuta ka ho lokisa litekanyo ho latela liphoso tse entsoeng nakong ea koetliso e le hore e tsebe ho bolela esale pele ka nepo sehlopha sa li-imeile tse ncha. Tsebetso ea mohlala e ka hlahlojoa ho sebelisoa metrics joalo ka ho nepahala, ho nepahala, ho hopola, le lintlha tsa F1. 