<prompt> Sisinya algorithm ea ho ithuta ka mochini e loketseng ho sekaseka lintlha tsa mongolo.nan  <response>: Instruction in English: Suggest a machine learning algorithm suitable for analyzing text data. . Response in English: One machine learning algorithm that is suitable for analyzing text data is the Naive Bayes algorithm. This algorithm is based on the Bayes theorem and is particularly useful when working with large datasets. It is widely used in text classification tasks, such as sentiment analysis and spam filtering, where the goal is to categorize text data into predefined classes. Naive Bayes algorithm works by calculating the probabilities of each class given the features in the input data and chooses the class with the highest probability. Another advantage of using Naive Bayes for text data analysis is its simplicity and efficiency, making it a good choice for scenarios where a quick and straightforward solution is needed. . Response in Sesotho: Algorithm e ngoe ea ho ithuta mochini e loketseng ho sekaseka data ea mongolo ke algorithm ea Naive Bayes. Algorithm ena e ipapisitse le theorem ea Bayes mme e bohlokoa haholo ha o sebetsa le li-dataset tse kholo. E sebelisoa haholo mesebetsing ea likarolo tsa mongolo, joalo ka tlhahlobo ea maikutlo le ho sefa li-spam, moo sepheo e leng ho arola lintlha tsa mongolo ka lihlopha tse boletsoeng esale pele. Naive Bayes algorithm e sebetsa ka ho bala menyetla ea sehlopha ka seng ho fanoe ka likarolo tsa data e kentsoeng mme e khetha sehlopha ka monyetla o phahameng ka ho fetisisa. Monyetla o mong oa ho sebelisa Naive Bayes bakeng sa tlhahlobo ea data ea mongolo ke bonolo le tsebetso ea eona, e etsa hore e be khetho e ntle bakeng sa maemo ao ho hlokahalang tharollo e potlakileng le e hlakileng. 