<prompt> Hlalosa mekhoa e meli ea ho ntlafatsa tsebetso ea mohlophisi oa mongolo.nan  <response>: Instruction in English: Describe two ways to improve the performance of a text classifier. . Response in English: 1. Preprocessing and Feature Engineering: One way to improve the performance of a text classifier is to preprocess the text data and engineer better features. Common preprocessing techniques include lowercasing, removing stop words, numbers and special characters, and tokenizing. Stemming and lemmatization may also be used. This process helps to clean the text data and reduce noise. Feature engineering techniques such as N-grams, term frequency-inverse document frequency TF-IDF, part-of-speech tagging, and sentiment analysis can be used to better represent the text and capture relationships between words.2. Choosing the Right Algorithm and Tuning Hyperparameters: Selection of the appropriate machine learning algorithm is also crucial. Different algorithms have different strengths and weaknesses, and their performance may vary depending on the characteristics of the dataset. Some common algorithms used in text classification are Naive Bayes, Support Vector Machines, and Random Forests. Tuning the hyperparameters of the selected algorithm can further improve the performance. Hyperparameters govern the behavior of the algorithm and can be optimized using techniques like grid search and cross-validation. . Response in Sesotho: 1. Preprocessing and Feature Engineering: Mokhoa o mong oa ho ntlafatsa tsebetso ea mohlahlobi oa mongolo ke ho lokisa esale pele data ea mongolo le likarolo tse ntle tsa boenjiniere. Mekhoa e tloaelehileng ea preprocessing e kenyelletsa ho fokotsa litlhaku tse tlaase, ho tlosa mantsoe a emisang, linomoro le litlhaku tse khethehileng, le tokenizing. Stemming le lemmatization le tsona li ka sebelisoa. Tsebetso ena e thusa ho hloekisa data ea mongolo le ho fokotsa lerata. Mekhoa ea boenjiniere ea likarolo tse kang N-grams, term frequency-inverse document frequency TF-IDF, ho tšoaea karolo ea puo, le tlhahlobo ea maikutlo li ka sebelisoa ho emela sengoloa hamolemo le ho hapa likamano lipakeng tsa mantsoe. 2. Ho Khetha Algorithm e Nepahetseng le Tuning Hyperparameters: Ho khethoa ha algorithm e nepahetseng ea ho ithuta mochine le eona ke ea bohlokoa. Li-algorithms tse fapaneng li na le matla le mefokolo e fapaneng, me tsebetso ea tsona e ka fapana ho latela litšobotsi tsa dataset. Li-algorithms tse ling tse tloaelehileng tse sebelisoang ho hlophisoa ha mongolo ke Naive Bayes, Mechini ea Tsehetso ea Vector, le Random Forests. Ho lokisa li-hyperparameter tsa algorithm e khethiloeng ho ka ntlafatsa tsebetso le ho feta. Li-hyperparameter li laola boitšoaro ba algorithm me li ka ntlafatsoa ho sebelisoa mekhoa e kang ho batla marang-rang le ho netefatsa. 