<prompt> Hlalosa bonyane mefuta e mehlano e tlwaelehileng ya leeme e fumanwang mefuteng ya ponelopele.nan  <response>: Instruction in English: Identify at least five common forms of bias found in predictive models. . Response in English: 1. Sampling Bias - occurs when the data used to train a predictive model is not representative of the population that the model will be making predictions for.2. Confirmation Bias - happens when data scientists or analysts unconsciously favor information or data that confirms their beliefs or hypotheses.3. Algorithmic Bias - this type of bias arises from the algorithms and models themselves, often as a result of flawed or biased assumptions built into the model.4. Measurement Bias - happens when the collection of data is done in a non-standardized, inconsistent or inaccurate manner resulting in skewed or biased data.5. Overfitting Bias - when a model is too complex and fits too closely to the training data, it may fail to generalize to new situations and could result in biased predictions. . Response in Sesotho: 1. Khethollo ea lisampole - e etsahala ha lintlha tse sebelisoang ho koetlisa mohlala oa ho lepa li sa emele palo ea batho bao mohlala o tla etsa likhakanyo tsa bona. 2. Confirmation Bias - e etsahala ha boramahlale ba data kapa bahlahlobisisi ba rata tlhahisoleseling kapa data e tiisang litumelo tsa bona kapa menahano ea bona ba sa tsebe. 3. Algorithmic Bias - mofuta ona oa khethollo o hlaha ho li-algorithms le mehlala ka boeona, hangata e le ka lebaka la likhopolo tse fosahetseng kapa tse leeme tse hahiloeng ho latela mohlala. 4. Measurement Bias - e etsahala ha pokello ea lintlha e etsoa ka mokhoa o sa tloaelehang, o sa lumellaneng kapa o sa nepahaleng o bakang lintlha tse sothehileng kapa tse leeme. 5. Overfitting Bias - ha mohlala o rarahane haholo me o lumellana haholo le lintlha tsa koetliso, o ka hlōleha ho akaretsa maemo a macha me o ka baka likhakanyo tse leeme. 