<prompt> Bolela metrics e 5 e tloaelehileng bakeng sa ho lekola mofuta oa ho ithuta mochininan  <response>: Instruction in English: Name 5 common metrics for evaluating a machine learning model . Response in English: The 5 common metrics used for evaluating machine learning models are:1. Accuracy: This measures the proportion of correctly classified instances to the total number of instances.2. Precision: This measures the proportion of predicted positive instances that are actually positive.3. Recall: Also known as sensitivity, this measures the proportion of actual positive instances that are correctly classified by the model.4. F1-Score: This is a weighted average of precision and recall, and is used when the class distribution is uneven.5. ROC-AUC: Receivers Operating Characteristic - Area Under Curve is a performance measurement for classification problems. It measures the ability of the model to distinguish between positive and negative classes. The larger the AOC, the better the models prediction accuracy. . Response in Sesotho: Melemo e 5 e tlwaelehileng e sebediswang bakeng sa ho lekola mefuta ya ho ithuta ka mochini ke: 1. Ho Nepaha: Sena se lekanya karolo ya maemo a hlophilweng ka nepo ho ya ho palo yohle ya maemo. 2. Ho nepahala: Sena se lekanya palo ea maemo a nepahetseng a boletsoeng esale pele a hlileng a leng ntle. 3. Hopola: E boetse e tsejoa e le kutloelo-bohloko, sena se lekanya karolo ea liketsahalo tse ntle tse hlophisitsoeng hantle ho latela mohlala. 4. F1-Score: Ena ke karolelano e lekantsoeng ea ho nepahala le ho hopola, me e sebelisoa ha kabo ea sehlopha e sa lekana. 5. ROC-AUC: Receivers Operating Characteristic - Area Under Curve ke tekanyo ea tshebetso bakeng sa mathata a lihlopha. E lekanya bokhoni ba mohlala ho khetholla pakeng tsa lihlopha tse ntle le tse mpe. Ha AOC e le kholo, e ntlafatsa ho nepahala ha polelo ea mohlala. 