<prompt> Mekhoa ea ho lekola mohlala oa ho ithuta mochini ke efe?nan  <response>: Instruction in English: What are the criteria for evaluating a machine learning model? . Response in English: Evaluating a machine learning model involves assessing how well it performs in terms of accuracy, precision, recall, and other relevant metrics. The specific criteria used can vary depending on the type of problem being addressed and the type of model being used. Here are some common criteria for evaluating a machine learning model:1. Accuracy: This measures the percentage of correct predictions made by the model.2. Precision: This measures the proportion of positive predictions that are actually true positive.3. Recall: This measures the proportion of actual positive instances that are correctly identified by the model.4. F1 Score: This is the harmonic mean of precision and recall, providing a single score that balances both metrics.5. Area under the Receiver Operating Characteristic ROC Curve: This summarizes the trade-off between the true positive rate and false positive rate for a classifier as the threshold is varied. 6. Mean Absolute Error MAE: This measures the average magnitude of the errors in a set of predictions, without considering their direction.7. Mean Squared Error MSE: This measures the average squared difference between the predicted and actual values, placing more weight on larger errors.8. Confusion Matrix: This is a table that shows the number of correct and incorrect predictions for each class, allowing for a more detailed analysis of the models performance.These are just some of the criteria that you can use to evaluate a machine learning model. Its important to choose the appropriate metrics for your specific problem and to use several different evaluation techniques to get a complete picture of the models performance. . Response in Sesotho: Ho lekola mofuta oa ho ithuta ka mochini ho kenyelletsa ho lekola hore na o sebetsa hantle hakae ho latela ho nepahala, ho nepahala, ho hopola, le metrics e meng e amehang. Litekanyetso tse khethehileng tse sebelisoang li ka fapana ho itšetlehile ka mofuta oa bothata bo rarolloang le mofuta oa mohlala o sebelisoang. Lintlha tse ling tse tloaelehileng tsa ho lekola mohlala oa ho ithuta ka mochini ke tsena: 1. Ho Nepaha: Sena se lekanya liperesente tsa likhakanyo tse nepahetseng tse entsoeng ke mofuta. 2. Nepahalo: Sena se lekanya karolo ea likhakanyo tse nepahetseng tseo ehlileng e leng tse ntle. 3. Hopola: Sena se lekanya palo ea liketsahalo tse ntle tse hloahloa ka nepo ke mohlala. 4. Lintlha tsa F1: Ena ke moelelo o lumellanang oa ho nepahala le ho hopola, ho fana ka lintlha tse le ngoe tse lekanyang metrics ka bobeli. 5. Sebaka se tlasa Receiver Operating Characteristic ROC Curve: Sena se akaretsa khoebo pakeng tsa sekhahla se nepahetseng sa nete le sekhahla se fosahetseng sa mohlahlobi kaha moeli o fapane. 6. Ho bolela Phoso e Felletseng MAE: Sena se lekanya boholo bo tloaelehileng ba liphoso ka sete sa likhakanyo, ntle le ho nahana ka tataiso ea bona. 7. Mean Squared Error MSE: Sena se lekanya karolelano ea phapang ea squared pakeng tsa litekanyetso tse boletsoeng esale pele le tsa sebele, ho beha boima bo bongata liphosong tse kholoanyane. 8. Matrix a Pherekano: Lena ke tafole e bontšang palo ea likhakanyo tse nepahetseng le tse fosahetseng tsa sehlopha ka seng, e leng se lumellang tlhahlobo e qaqileng haholoanyane ea tsebetso ea mohlala. Tsena ke tse ling tsa mekhoa eo u ka e sebelisang ho lekola mohlala oa ho ithuta oa mochini. Ho bohlokoa ho khetha litekanyetso tse nepahetseng bakeng sa bothata ba hau le ho sebelisa mekhoa e fapaneng ea tlhahlobo ho fumana setšoantšo se felletseng sa tsebetso ea mohlala. 