<prompt> Ngola mokhoa oa ho hlophisa ho fumana mangolo-tsoibila a spam.Ona ke mohlala oa lengolo-tsoibila: Sehlooho: Nyehelo ea papatso Moreki ea Ratehang, Re na le mpho e khethehileng eo re u boloketseng eona! Fumana phokoletso ya 50 ha o reka kajeno. Tobetsa sehokelo se ka tlase ho tseba haholoanyane.  <response>: Instruction in English: Write a classification algorithm to detect spam emails. . Response in English: Here is a simple classification algorithm that can be used to detect spam emails:1. Data collection and preprocessing: Collect a dataset of both legitimate ham and spam emails. Preprocess the data by tokenizing the text and removing stop words and special characters. Convert the text into numerical features using techniques like Bag-of-Words or TF-IDF.2. Feature Selection: Select the most relevant features that can help distinguish between ham and spam emails. You can use techniques like Information Gain, Chi-square Test or Mutual Information. Typical features used in spam detection include the frequency of specific words, the length of the email, the presence of certain characters like or ! and the use of capital letters.3. Model Training: Train a classifier on the preprocessed dataset. You can use classification algorithms like Naive Bayes, Decision Trees, Random Forest or Logistic Regression. Use cross-validation techniques to evaluate the performance of the model and tune any hyperparameters.4. Prediction: Given a new email, use the trained classifier to predict whether it is a spam or ham. In the example email given above, the classifier would process the text and return a label indicating if it is a spam email or not.This is a basic outline of the steps involved in building a spam email classifier. The actual implementation may vary depending on the choice of features, preprocessing techniques, and classification algorithms used. . Response in Sesotho: Mona ke algorithm e bonolo ea ho arola e ka sebelisoang ho bona li-imeile tsa spam: 1. Pokello ea data le ho e lokisa: Bokella pokello ea lintlha tsa li-imeile tse molaong ham le tsa spam. Tsoela pele ho data ka ho tšoaea mongolo le ho tlosa mantsoe a emisang le litlhaku tse ikhethileng. Fetolela mongolo ho likarolo tsa lipalo u sebelisa mekhoa e kang Bag-of-Words kapa TF-IDF. 2. Khetho ea Litšobotsi: Khetha likarolo tse loketseng tse ka u thusang ho khetholla pakeng tsa li-imeile tsa ham le tsa spam. U ka sebelisa mekhoa e kang Phaello ea Boitsebiso, Teko ea Chi-square kapa Boitsebiso ba Mutual. Likarolo tse tloahelehileng tse sebelisoang ho lemoha spam li kenyelletsa makhetlo a mangata a mantsoe a ikhethileng, bolelele ba lengolo-tsoibila, boteng ba litlhaku tse itseng joalo ka kapa ! le tšebeliso ea litlhaku tse kholo. 3. Koetliso ea Mohlala: Koetlisa mohlophisi ho dataset e sebelitsoeng esale pele. U ka sebelisa li-algorithms tsa lihlopha tse kang Naive Bayes, Lifate tsa Qeto, Meru e sa Lebelloang kapa Logistic Regression. Sebelisa mekhoa e fapaneng ea netefatso ho lekola tsebetso ea mohlala le ho tsamaisa li-hyperparameter life kapa life. 4. Prediction: Ha u fuoa lengolo-tsoibila le lecha, sebelisa sehlopha se koetlisitsoeng ho bolela esale pele hore na ke spam kapa ham. Mohlala oa lengolo-tsoibila le fanoeng ka holimo, mokhethoa o tla sebetsana le mongolo ebe o khutlisa lengolo le bontšang hore na ke lengolo-tsoibila la spam kapa che. Ena ke kemiso ea mantlha ea mehato e amehang ho aheng sehlopha sa lengolo-tsoibila la spam. Tsebetsong ea nete e ka fapana ho latela khetho ea likarolo, mekhoa ea ho lokisa esale pele, le li-algorithms tsa lihlopha tse sebelisitsoeng. 