Reporting bias: Difference between revisions

'''Reporting bias''' in machine learning is a systematic distortion of the information used to train and evaluate machine learning models. This distortion arises when the data being used to train a model is influenced by factors that are not representative of the true underlying phenomenon. These factors can lead to an overestimation or underestimation of certain model predictions, ultimately affecting the performance and reliability of the model. This article will discuss the causes and implications of reporting bias, as well as strategies to mitigate its effects.

Reporting bias can stem from several sources, including data collection methods, sample selection, and data preprocessing.