MerkmalsauswahlBias
Merkmalsauswahlbias, also known as feature selection bias, refers to a systematic error in statistical modeling or data analysis that arises when certain variables (features) are preferentially included or excluded during the process of selecting predictors for a model. This bias can distort the results and lead to misleading conclusions, particularly in fields such as machine learning, epidemiology, and economics.
The phenomenon occurs when researchers or algorithms favor specific features based on subjective criteria rather than
A common cause of Merkmalsauswahlbias is the use of correlation-based feature selection methods, where only highly
To mitigate Merkmalsauswahlbias, researchers should employ rigorous validation techniques, such as cross-validation, to ensure that selected