<prompt> Hlahisa mekhoa e meli e nepahetseng le e fapaneng ea ho rarolla bothata bo fanoeng.Ho bolela esale pele hore na lengolo-tsoibila ke spam kapa che.  <response>: Instruction in English: Propose two appropriate and different methods to solve the given problem. . Response in English: 1. Naive Bayes Classifier: One approach to predicting whether an email is spam or not is to use a Naive Bayes Classifier, a probabilistic-based algorithm for classification tasks. This method works by calculating the likelihood that an email is spam based on the frequencies of words and phrases it contains and comparing it to the probability that it is not spam. By utilizing a training set containing both spam and non-spam emails, the classifier calculates the probability that an email with a particular set of features words and phrases is a spam or not spam. After the classifier has been trained, it can make predictions for new emails by calculating the posterior probabilities and choosing the label with the highest probability.2. Support Vector Machines SVM: Another method to tackle this problem is by using Support Vector Machines SVM, a popular algorithm used for binary classification. In the context of spam email detection, SVM works by finding a hyperplane that optimally separates the training data into two classes - spam and non-spam emails. The algorithm achieves this by mapping the emails into a high-dimensional feature space and determining the hyperplane that maximizes the margin between the two classes. After the model has been trained, it can classify new emails by determining which side of the hyperplane the email falls on, classifying it as either spam or not. . Response in Sesotho: 1. Naive Bayes Classifier: Mokhoa o mong oa ho lepa hore na lengolo-tsoibila ke spam kapa che ke ho sebelisa Naive Bayes Classifier, algorithm e thehiloeng ho probabilistic bakeng sa mesebetsi ea lihlopha. Mokhoa ona o sebetsa ka ho bala monyetla oa hore lengolo-tsoibila ke spam ho latela makhetlo a mangata a mantsoe le lipoleloana tseo e nang le tsona le ho li bapisa le monyetla oa hore ha se spam. Ka ho sebelisa sehlopha sa koetliso se nang le li-imeile tsa spam le tseo e seng tsa spam, mokhethoa o lekanya monyetla oa hore lengolo-tsoibila le nang le likarolo tse itseng mantsoe le lipolelo ke spam kapa eseng spam. Ka mora hore mohlophisi a koetlisoe, a ka etsa likhakanyo bakeng sa mangolo-tsoibila a macha ka ho bala menyetla e ka morao le ho khetha leibole ka monyetla o phahameng ka ho fetisisa. 2. Support Vector Machines SVM: Mokhoa o mong oa ho sebetsana le bothata bona ke ka ho sebelisa Support Vector Machines SVM, e leng algorithm e tsebahalang e sebelisoang bakeng sa lihlopha tsa binary. Boemong ba ho fumana lengolo-tsoibila la spam, SVM e sebetsa ka ho fumana hyperplane e arolang lintlha tsa koetliso ka lihlopha tse peli - li-imeile tsa spam le tseo e seng tsa spam. Algorithm e finyella sena ka ho etsa mapa oa li-imeile sebakeng se phahameng sa likarolo le ho khetholla hyperplane e eketsang moeli pakeng tsa lihlopha tse peli. Kamora hore mofuta ona o koetlisoe, o ka hlophisa li-imeile tse ncha ka ho tseba hore na lengolo-tsoibila le oela ka lehlakoreng lefe la hyperplane, e e khetholla e le spam kapa che. 