<prompt> Hlalosa se boleloang ke bias-variance trade-off.nan  <response>: Instruction in English: Explain what is meant by the bias-variance trade-off. . Response in English: The bias-variance trade-off is a fundamental concept in statistical learning and machine learning, that refers to the balance that must be achieved between two sources of error when building a predictive model. Bias refers to errors that arise from the assumptions made by the model, while variance refers to errors that arise from sensitivity to small fluctuations in the training data. In other words, a model with high bias makes strong assumptions about the relationship between the input and output variables and may miss important relationships, whereas a model with high variance may be too flexible and fit the training data too closely, including any noise or random fluctuations. Ideally, we want to create a model that has low bias and low variance, but in practice, there is a trade-off between these two sources of error. If a model is too simple, it may not capture important relationships in the data, leading to high bias. Conversely, if a model is too complex, it may become overly sensitive to the training data and fit it too closely, leading to high variance. The key to achieving a good bias-variance trade-off is to find a balance between model complexity and ability to generalize to new data, through techniques such as regularization or cross-validation. In summary, the bias-variance trade-off is the struggle to balance the desire for a model to have enough flexibility to accurately capture relationships in the data, while also having enough stability to work well on new, unseen data. . Response in Sesotho: Khoebo ea bias-variance trade-off ke mohopolo oa motheo thutong ea lipalo-palo le ho ithuta ka mochini, e bolelang ho leka-lekana ho lokelang ho finyelloa pakeng tsa mehloli e meli ea phoso ha ho etsoa mohlala oa ho bolela esale pele. Leeme le bolela liphoso tse hlahang menahanong e entsoeng ke mohlala, ha phapano e bua ka liphoso tse hlahang ho tloha ho kutloelo-bohloko ho ea ho liphetoho tse nyane tsa data ea koetliso. Ka mantsoe a mang, mohlala o nang le leeme le phahameng o fana ka maikutlo a matla mabapi le kamano pakeng tsa mefuta e fapaneng ea ho kenya le ea tlhahiso me e ka na ea lahleheloa ke likamano tsa bohlokoa, athe mohlala o nang le phapang e phahameng o ka fetoha habonolo me oa lumellana le lintlha tsa koetliso haufi-ufi, ho kenyeletsa lerata leha e le lefe kapa ka tšohanyetso. ho feto-fetoha ha maemo. Ka mokhoa o nepahetseng, re batla ho theha mohlala o nang le khethollo e tlaase le phapang e fokolang, empa ka tsebetso, ho na le khoebo pakeng tsa mehloli ena e meli ea phoso. Haeba mohlala o bonolo haholo, o ka na oa se ke oa tšoara likamano tsa bohlokoa ho data, e leng se lebisang ho khethollo e phahameng. Ka lehlakoreng le leng, haeba mohlala o rarahane haholo, o ka na oa ameha haholo ho data ea koetliso me oa o lekana haufi-ufi, o lebisang ho phapano e phahameng. Senotlolo sa ho finyella khoebo e ntle ea leeme-ho fapana ke ho fumana ho leka-lekana pakeng tsa ho rarahana ha mohlala le bokhoni ba ho akaretsa lintlha tse ncha, ka mekhoa e kang ea kamehla kapa ho netefatsa. Ka kakaretso, khoebo ea leeme-ho fapana ke ntoa ea ho leka-lekanya takatso ea hore mohlala o be le phetoho e lekaneng ea ho tšoara ka nepo likamano tsa data, ha o ntse o e-na le botsitso bo lekaneng ba ho sebetsa hantle ho data e ncha, e sa bonahaleng. 