<prompt> Hlalosa tsebetso le tsebeliso ea tsebetso ea tahlehelo.nan  <response>: Instruction in English: Describe the function and use of a loss function. . Response in English: A loss function, also known as a cost function or objective function, is a mathematical function used in machine learning and artificial intelligence systems to evaluate the performance of predictive models. Its purpose is to measure how well a model is able to predict the expected outcomes, by calculating the difference between the predicted values and the actual values.The loss function assigns a numerical value to represent the cost or penalty associated with the prediction error. This value is then used by an optimization algorithm to adjust the parameters of the model, in order to minimize the loss and improve the accuracy of future predictions.In essence, the loss function acts as a guide for the model, pointing it towards the direction of lowest error and best fit with the data. Different kinds of loss functions are used depending on the type of problem being solved and the nature of the data. For instance, common loss functions used in regression problems include Mean Squared Error and Mean Absolute Error, while in classification problems, Cross Entropy and Hinge loss are commonly used.Overall, the use of a loss function is a crucial part in the process of training machine learning models, as it helps to measure the quality of the predictions and provides a basis for improving the models performance. . Response in Sesotho: Mosebetsi oa tahlehelo, o tsejoang hape e le tsebetso ea litšenyehelo kapa sepheo, ke tsebetso ea lipalo e sebelisoang thutong ea mochini le litsebetso tsa bohlale ba maiketsetso ho lekola tsebetso ea mefuta e lebelloang esale pele. Sepheo sa eona ke ho lekanya hore na mohlala o khona ho bolela esale pele liphello tse lebeletsoeng hakae, ka ho bala phapang pakeng tsa litekanyetso tse boletsoeng esale pele le litekanyetso tsa sebele. Mosebetsi oa tahlehelo o fana ka boleng ba linomoro ho emela litšenyehelo kapa kotlo e amanang le phoso ea ho bolela esale pele. Joale boleng bona bo sebelisoa ke algorithm ea optimization ho lokisa liparamente tsa mohlala, e le ho fokotsa tahlehelo le ho ntlafatsa ho nepahala ha likhakanyo tsa nako e tlang. Ha e le hantle, tsebetso ea tahlehelo e sebetsa e le tataiso bakeng sa mohlala, e e lebisang moo ho nang le phoso e tlaase le ho lumellana hantle le data. Mefuta e fapaneng ea mesebetsi ea tahlehelo e sebelisoa ho latela mofuta oa bothata bo rarolloang le mofuta oa data. Mohlala, litsebetso tse tloaelehileng tsa tahlehelo tse sebelisoang mathateng a ho khutlisa li kenyelletsa Mean Squared Error le Mean Absolute Error, ha mathata a lihlopha, Cross Entropy le Hinge loss li sebelisoa hangata. Ka kakaretso, tsebeliso ea tsebetso ea tahlehelo ke karolo ea bohlokoa molemong oa ho koetlisa mehlala ea ho ithuta mochini, kaha e thusa ho lekanya boleng ba likhakanyo le ho fana ka motheo oa ho ntlafatsa tsebetso ea mohlala. 