<prompt> Mefuta ea ho ithuta ka mochini e ka hlahlojoa joang?nan  <response>: Instruction in English: How can machine learning models be evaluated? . Response in English: Machine Learning models can be evaluated using various methods and techniques depending on the type of model and the problem it aims to solve. The goal is to measure the performance of the model and determine how well it generalizes to new data. Below are some common techniques for evaluating machine learning models:1. Splitting data: One of the most common methods for evaluating the model is by splitting the data into training and testing sets. The data is typically split 7030 or 8020, meaning that 70 or 80 of the data is used for training and 30 or 20 for testing.2. Cross-validation: This is a statistical method where the dataset is split into k-folds, and the model is trained and tested k times. Each time, a different fold of the data is used for testing, while the remaining parts are used for training. This method helps to reduce the variance and provides better accuracy for the model.3. Metrics: Various metrics can be used to measure the performance of the model, and the choice of metric depends on the type of problem. For classification models, accuracy, precision, recall, F1-score, and confusion matrix are commonly used. For regression models, mean absolute error, mean squared error, root mean squared error, and R-squared are popular metrics.4. ROC and AUC: The Receiver Operating Characteristic ROC curve plots the true positive rate against the false-positive rate for varying thresholds. It is used to evaluate the performance of binary classifiers. The area under the ROC curve AUC represents the probability that the classifier will rank a randomly chosen positive instance higher than a randomly chosen negative instance.In conclusion, the evaluation of machine learning models is an important step to ensure that the model performs well on new unseen data. It provides insights into the models strengths and weaknesses and guides the selection of the most appropriate model for the problem at hand. . Response in Sesotho: Mehlala ea ho Ithuta ka Mechini e ka hlahlojoa ho sebelisoa mekhoa le mekhoa e fapaneng ho latela mofuta oa mohlala le bothata boo e ikemiselitseng ho bo rarolla. Sepheo ke ho lekanya tsebetso ea mohlala le ho fumana hore na e sebetsa hantle hakae ho data e ncha. Ka tlase ke mekhoa e meng e tloaelehileng ea ho hlahloba mekhoa ea ho ithuta ka mochine: 1. Ho arola lintlha: E ngoe ea mekhoa e tloaelehileng ea ho hlahloba mohlala ke ka ho arola boitsebiso ka lihlopha tsa koetliso le liteko. Lintlha hangata li arotsoe ka 7030 kapa 8020, ho bolelang hore 70 kapa 80 ea data e sebelisoa bakeng sa koetliso le 30 kapa 20 bakeng sa tlhahlobo. 2. Thibelo e fapaneng: Ena ke mokhoa oa lipalo-palo moo dataset e arotsoeng ka mameno a k, me mohlala oa koetlisoa le ho lekoa ka makhetlo a k. Nako le nako, ho sebelisoa lesaka le fapaneng la data bakeng sa tlhahlobo, ha likarolo tse setseng li sebelisetsoa koetliso. Mokhoa ona o thusa ho fokotsa phapang le ho fana ka ho nepahala ho molemo bakeng sa mohlala. 3. Metrics: Metrics e fapaneng e ka sebelisoa ho lekanya tsebetso ea mohlala, me khetho ea metric e itšetlehile ka mofuta oa bothata. Bakeng sa mehlala ea lihlopha, ho nepahala, ho nepahala, ho hopola, F1-score, le matrix a pherekano a atisa ho sebelisoa. Bakeng sa mefuta ea regression, ho bolela phoso e felletseng, phoso e bolelang hore e na le squared, root mean squared error, le R-squared ke metrics e tsebahalang. 4. ROC le AUC: The Receiver Operating Characteristic ROC curve e rala sekhahla se nepahetseng sa nete khahlanong le sekhahla se fosahetseng bakeng sa menyako e sa tšoaneng. E sebelisetsoa ho lekola tsebetso ea li-binary classifiers. Sebaka se tlasa ROC curve AUC se emela monyetla oa hore mohlophisi o tla beha boemo bo nepahetseng bo khethiloeng ka tšohanyetso ho feta mohlala o mobe o khethiloeng ka tšohanyetso. Qetellong, tlhahlobo ea mekhoa ea ho ithuta mochine ke mohato oa bohlokoa ho netefatsa hore mohlala o sebetsa hantle ho data e ncha e sa bonahaleng. E fana ka leseli mabapi le matla le mefokolo ea mohlala le ho tataisa khetho ea mohlala o nepahetseng ka ho fetisisa bakeng sa bothata bo teng. 