<prompt> Hlahisa lethathamo la lipotso tse ka sebelisoang ho lekola mohlala oa ho ithuta oa mochini.nan  <response>: Instruction in English: Generate a list of questions that can be used to evaluate a machine learning model. . Response in English: 1. What is the performance of the model on the training data and validation data?2. Has the model been tested for overfitting or underfitting?3. How does the models performance compare to other models trained on the same data?4. Can the model generalize well on unseen data?5. What evaluation metrics have been used and how was the models performance in each metric?6. Was cross-validation used to evaluate the model?7. How does the model handle class imbalance, if present in the training data?8. Is the model able to make predictions within an acceptable time frame for practical use?9. Is the model able to interpret and explain its predictions?10. How does the model handle missing or incomplete data, and what steps were taken to preprocess the data before training?11. Were any techniques used to improve model performance, such as feature selection, hyperparameter tuning, or ensemble methods?12. How sensitive is the models performance to changes in the data, and is it robust to variations in the input data?13. Are there any ethical or fairness issues that arise from the models predictions, and how have they been addressed?14. Is the model scalable, and can it handle larger and more complex datasets?15. How easy is it to update and maintain the model as new data becomes available? . Response in Sesotho: 1. Tsebetso ea mohlala ho data ea koetliso le data ea netefatso ke efe? 2. Na mohlala o lekoa ho feta tekano kapa ho se sebetse hantle? 3. Tsebetso ea mohlala e bapisoa joang le mefuta e meng e koetlisitsoeng ka data e tšoanang? 4. Na mohlala o ka akaretsa hantle ho data e sa bonoeng? 5. Ke metrics efe ea tlhahlobo e sebelisitsoeng, me tsebetso ea mohlala e bile joang ho metric ka ngoe? 6. Na ho sebelisitsoe netefatso e fapaneng ho lekola mohlala? 7. Mohlala o sebetsana joang le ho se leka-lekane ha sehlopha, haeba ho le teng boitsebisong ba koetliso? 8. Na mohlala o khona ho bolela esale pele ka nako e amohelehang bakeng sa tšebeliso e sebetsang? 9. Na mohlala o khona ho hlalosa le ho hlalosa lintho tse boletsoeng esale pele? 10. Mohlala o sebetsana joang le lintlha tse sieo kapa tse sa fellang, me ke mehato efe e nkiloeng ho lokisa boitsebiso pele ho koetliso? 11. Na ho na le mekhoa e sebelisitsoeng ho ntlafatsa tsebetso ea mohlala, joalo ka khetho ea likarolo, hyperparameter tuning, kapa mekhoa ea ho kopanya? 12. Tsebetso ea mohlala e hlokolosi hakae liphetohong tsa data, me na e matla ho fapana ha data ea ho kenya? 13. Na ho na le litaba tsa boitšoaro kapa tse hlokang leeme tse hlahisoang ke se boletsoeng esale pele ke mohlala, hona li se li rarollotsoe joang? 14. Na mohlala o ka senyeha, me na o khona ho sebetsana le li-dataset tse kholoanyane le tse rarahaneng? 15. Ho bonolo hakae ho ntlafatsa le ho boloka mohlala ha data e ncha e fumaneha? 