<prompt> Rala algorithm ea likarolo ho bona molaetsa e le spam kapa ham.Ha ho tlhahiso  <response>: Instruction in English: Design a classification algorithm to detect a message as spam or ham. . Response in English: Spam detection is a binary classification problem where messages are classified as either spam or ham not-spam. A classification algorithm can be used to learn from a pre-labeled training dataset to determine if an incoming message is spam or ham. Here are the steps to design a spam detection algorithm:1. Data Collection: Collect a large dataset of messages along with their labels as spam or ham. This dataset will be used to train our classification model.2. Preprocessing: Preprocess the text data by removing any special characters, numbers, and punctuations. Convert all the text to lowercase and tokenize the messages into individual words.3. Feature Extraction: One way to represent text data for machine learning is by converting it into a numerical format. This can be done by using Bag of Words or Term Frequency-Inverse Document Frequency TF-IDF techniques. These techniques results in a sparse matrix representing the frequency of words in the messages.4. Model Selection: Choose a classification algorithm to train on the data. Some commonly used algorithms for spam detection are Naive Bayes, Support Vector Machines, and Random Forest. Use cross-validation techniques to evaluate the performance of the algorithm.5. Training: Use the preprocessed and vectorized data to train the classifier. The classifier will learn the relationship between the features and the outcome variable spam or ham from the training data.6. Prediction: Once the model is trained, it can be used to predict whether a new message is spam or ham. This prediction is based on the features extracted from the message and the learned relationship between the features and outcome variable.7. Evaluation: Evaluate the performance of the classifier on a separate test dataset. Some commonly used evaluation metrics are accuracy, precision, recall and F1-score.8. Fine-tuning: Fine-tune the classifier by modifying the hyperparameters, using different feature extraction techniques, or by using an ensemble of models to improve the performance.In summary, designing a spam detection algorithm involves collecting and preprocessing the data, extracting relevant features, selecting and training a classification model, making predictions, evaluating the performance, and fine-tuning the model. . Response in Sesotho: Ho lemoha spam ke bothata ba ho arola batho ka bongata moo melaetsa e hlalosoang e le spam kapa ham eseng spam. Algorithm ea lihlopha e ka sebelisoa ho ithuta ho tswa ho dataset ea koetliso e ngotsoeng esale pele ho fumana hore na molaetsa o kenang ke spam kapa ham. Mehato ea ho etsa algorithm ea ho lemoha spam ke ena: 1. Pokello ea Lintlha: Bokella lethathamo le leholo la melaetsa hammoho le lileibole tsa bona joalo ka spam kapa ham. Lethathamo lena la data le tla sebelisoa ho koetlisa mofuta oa rona oa lihlopha. 2. Ho lokisa esale pele: Lokisetsa data ea mongolo esale pele ka ho tlosa litlhaku tse khethehileng, linomoro le matšoao. Fetolela mongolo ohle hore e be litlhaku tse tlase le tokenize melaetsa hore e be mantsoe ka bomong. 3. Phatlalatso ea Tšobotsi: Tsela e ngoe ea ho emela lintlha tsa mongolo bakeng sa ho ithuta ka mochine ke ka ho li fetolela ka mokhoa oa linomoro. Sena se ka etsoa ka ho sebelisa mekhoa ea Bag of Words kapa Term Frequency-Inverse Document Frequency TF-IDF. Mekhoa ena e fella ka matrix a fokolang a emelang makhetlo a mangata melaetsa. 4. Khetho ea Mohlala: Khetha algorithm ea lihlopha ho koetlisa ho data. Mekhoa e meng e sebelisoang hangata bakeng sa ho lemoha spam ke Naive Bayes, Support Vector Machines, le Random Forest. Sebelisa mekhoa e fapaneng ea netefatso ho lekola tsebetso ea algorithm. 5. Koetliso: Sebelisa lintlha tse entsoeng esale pele le tse hlahisitsoeng ke vectorized ho koetlisa mohlophisi. Sehlopha se tla ithuta kamano pakeng tsa likarolo le phetoho ea sephetho spam kapa ham ho tsoa ho data ea koetliso. 6. Ho bolela esale pele: Hang ha mohlala o koetlisitsoe, o ka sebelisoa ho bolela esale pele hore na molaetsa o mocha ke spam kapa ham. Polelo ena e ipapisitse le likarolo tse ntšitsoeng molaetseng le kamano e ithutoang lipakeng tsa likarolo le phapang ea sephetho. 7. Tekolo: Lekola tsebetso ea mokhethoa ho dataset ea tlhahlobo e arohaneng. Tse ling tsa metrics tse sebelisoang hangata ke ho nepahala, ho nepahala, ho hopola le lintlha tsa F1. 8. Tokiso e ntle: Beakanya sehlopha ka ho fetola li-hyperparameter, u sebelisa mekhoa e fapaneng ea ho ntša likarolo, kapa ka ho sebelisa mefuta e mengata ho ntlafatsa tshebetso. Ka bokhutšoanyane, ho etsa algorithm ea ho lemoha spam ho kenyelletsa ho bokella le ho lokisa lintlha esale pele, ho ntša lintlha tse amehang, ho khetha le ho koetlisa mohlala oa lihlopha, ho bolela esale pele, ho hlahloba tsebetso, le ho lokisa mohlala hantle. 