<prompt> Fana ka litekanyetso tsa ho hlahloba mefuta ea ho ithuta ea mochini.nan  <response>: Instruction in English: Provide a set of criteria for evaluating machine learning models. . Response in English: Machine learning models need to be evaluated to determine their effectiveness and accuracy. The following are common criteria used when evaluating machine learning models:1. Accuracy: The accuracy of the model is a measure of how well it performs on a given set of data. The higher the accuracy, the better the model is at correctly classifying or predicting the data.2. Confusion matrix: A confusion matrix is a table that shows the number of correctly and incorrectly classified instances. It is used to determine the models precision, recall, and F1-score.3. Precision: Precision is the ratio of true positive instances to the total number of positive instances. A high precision means that the model is good at correctly identifying true positives and avoiding false positives.4. Recall: Recall is the ratio of true positive instances to the total number of instances that were actually positive. A high recall means that the model is good at finding all the positive instances.5. F1-score: The F1-score is the harmonic mean of precision and recall. It represents the balance between precision and recall and is used when both are important.6. ROC curve: The receiver operating characteristic ROC curve is a graphical representation of the relationship between the true positive rate and the false positive rate. The area under the ROC curve AUC is used as a measure of the models ability to discriminate between positive and negative instances.7. Mean squared error MSE: Mean squared error is used to measure the average of the squared errors between the predicted values and the actual values. It is commonly used for regression problems.8. Mean absolute error MAE: Mean absolute error is the average of the absolute differences between the predicted and actual values. It is also commonly used for regression problems.9. Overfitting and underfitting: Overfitting occurs when a model is too complex and memorizes the training data, while underfitting occurs when a model is too simple and cannot capture the underlying patterns in the data. The model should be evaluated to ensure that it is neither overfitting nor underfitting.10. Cross-validation: Cross-validation is a technique used to assess the models ability to generalize to unseen data. It involves dividing the data into several folds and training the model on all but one fold, then evaluating the model on the remaining fold. This is repeated for each fold, and the average performance is used as a measure of the models effectiveness.Overall, the criteria used to . Response in Sesotho: Mehlala ea ho ithuta ka mochini e hloka ho hlahlobisisoa ho fumana katleho le ho nepahala ha eona. Tse latelang ke mekhoa e tloaelehileng e sebelisoang ha ho hlahlojoa mekhoa ea ho ithuta ka mochine: 1. Ho nepahala: Ho nepahala ha mohlala ke tekanyo ea hore na o sebetsa hantle hakae ho sehlopha se itseng sa data. Ha ho nepahala ho phahame, mohlala o betere ho hlophisa kapa ho bolela lintlha esale pele. 2. Matrix a Pherekano: Matrix a pherekano ke tafole e bontšang palo ea maemo a khethiloeng ka nepo le ka mokhoa o fosahetseng. E sebelisoa ho fumana bonnete ba mohlala, ho hopola, le lintlha tsa F1. 3. Ho nepahala: Ho nepahala ke karo-karolelano ea liketsahalo tse ntle ho palo eohle ea liketsahalo tse ntle. Ho nepahala ho phahameng ho bolela hore mohlala o sebetsa hantle ho tsebahatseng lintho tse nepahetseng le ho qoba maikutlo a fosahetseng. 4. Hopola: Hopola ke karo-karolelano ea liketsahalo tse ntle ho palo eohle ea liketsahalo tse hlileng li leng ntle. Ho hopola ho hoholo ho bolela hore mohlala o motle ho fumana maemo ohle a matle. 5. F1-score: F1-score ke moelelo oa harmonic oa ho nepahala le ho hopola. E emela ho leka-lekana pakeng tsa ho nepahala le ho hopola me e sebelisoa ha bobeli e le ba bohlokoa. 6. ROC Curve: The receiver operating character ROC curve ke setšoantšo sa setšoantšo sa kamano pakeng tsa sekhahla sa nete se nepahetseng le sekhahla se fosahetseng. Sebaka se tlasa ROC curve AUC se sebelisoa e le tekanyo ea bokhoni ba mohlala ba ho khetholla pakeng tsa maemo a matle le a mabe. 7. Mean squared error MSE: Mean squared error MSE: Mean squared error e sebelisoa ho metha kakaretso ea liphoso tse lisekoere pakeng tsa boleng bo boletsoeng esale pele le boleng ba sebele. Hangata e sebelisoa bakeng sa mathata a ho khutlela morao. 8. Ho bolela phoso e feletseng MAE: Ho bolela phoso e feletseng ke karolelano ea phapang e feletseng pakeng tsa litekanyetso tse boletsoeng esale pele le tsa sebele. E boetse e sebelisoa hangata bakeng sa mathata a ho khutlela morao. 9. Overfitting le underfitting: Overfitting e etsahala ha mohlala o rarahane haholo me o tšoara lintlha tsa koetliso ka hlooho, ha underfitting e etsahala ha mohlala o le bonolo haholo me o sitoa ho nka mekhoa e ka tlaase ho data. Mohlala o lokela ho hlahlobjoa ho netefatsa hore ha o fetelletse kapa o sa lekana. 10. Tiisetso ka ho fapanyetsana: Tiiso e fapaneng ke thekeniki e sebediswang ho lekola bokgoni ba mmotlolo ho akaretsa ditaba tse sa bonahaleng. E kenyelletsa ho arola lintlha ka mameno a maloa le ho koetlisa mohlala ho bohle ntle le lesapo le le leng, ebe ho lekola mohlala lesakeng le setseng. Sena se phetoa bakeng sa lesaka le leng le le leng, me karolelano ea tsebetso e sebelisoa e le tekanyo ea katleho ea mohlala. Ka kakaretso, litekanyetso tse sebelisoang ho 