<prompt> Hlalosa mehato e mehlano ea lipalo-palo e bohlokoa bakeng sa ho lekola tšebetso ea mohlala oa ho ithuta ka mochininan  <response>: Instruction in English: Identify five statistical measures that are important for evaluating the performance of a machine learning model . Response in English: 1. Accuracy: This is the simplest and most commonly used measure of a classification models performance. Its calculated by dividing the number of correctly predicted examples by the total number of examples in the dataset.2. Precision: Precision is a measure of a models ability to identify only relevant instances, and is typically used in scenarios where false positives are more costly than false negatives. Its calculated by dividing the number of true positives by the total number of true positives and false positives.3. Recall: Recall, also known as sensitivity, is a measure of a models ability to identify all relevant instances. Its calculated by dividing the number of true positives by the total number of true positives and false negatives, and is typically used in scenarios where false negatives are more costly than false positives.4. F1-Score: F1-score is the harmonic mean of precision and recall, and is a measure of a models overall performance. Its calculated by taking the product of precision and recall and dividing it by the sum of precision and recall, then multiplying the result by two. It is used in situations where both precision and recall are important.5. Area Under the Curve AUC: AUC is a measure of a models performance across all possible classification thresholds, and is calculated by plotting the true positive rate against the false positive rate at different threshold values. It is used in scenarios where the model must make a trade-off between maximizing true positives and minimizing false positives. . Response in Sesotho: 1. Ho Nepaha: Ena ke tekanyo e bonolo le e sebelisoang hangata ea tšebetso ea mofuta oa lihlopha. E baloa ka ho arola palo ea mehlala e boletsoeng esale pele ka kakaretso ea mehlala lethathamong la lintlha. 2. Nepaha: Ho nepahala ke tekanyo ea bokhoni ba mohlala oa ho tseba maemo a amehang feela, me hangata e sebelisoa maemong ao mashano a fosahetseng a bitsang chelete e ngata ho feta a fosahetseng. E baloa ka ho arola palo ea likhetho tsa nete ka kakaretso ea lintlha tse nepahetseng le tse fosahetseng. 3. Hopola: Hopola, hape e tsejwang e le sensitivity, ke tekanyo ya bokgoni ba mmotlolo ho hlwaya maemo ohle a amehang. E baloa ka ho arola palo ea lintho tse nepahetseng ka kakaretso ea li-positives tsa nete le tse fosahetseng, me hangata e sebelisoa maemong ao li-negative tsa bohata li bitsang chelete e ngata ho feta tse fosahetseng. 4. F1-Score: F1-score ke moelelo o lumellanang oa ho nepahala le ho hopola, me ke tekanyo ea tsebetso ea kakaretso ea mohlala. E baloa ka ho nka sehlahisoa se nepahetseng le ho se hopola le ho se arola ka kakaretso ea ho nepahala le ho hopola, ebe se atisa sephetho ka tse peli. E sebelisoa maemong ao ho ona ho nepahetseng le ho hopola ho leng bohlokoa. 5. Area Under the Curve AUC: AUC ke tekanyo ea tsebetso ea mohlala ho mekhahlelo eohle e ka bang teng ea ho arola, me e baloa ka ho rera sekgahla se nepahetseng sa nete khahlano le sekgahla se fosahetseng ka litekanyetso tse fapaneng. E sebelisoa maemong ao ho ona mohlala o tlamehang ho etsa khoebo pakeng tsa ho eketsa melemo ea nete le ho fokotsa maikutlo a fosahetseng. 