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Reporting bias: Difference between revisions
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
'''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 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. | |||
==Causes of Reporting Bias== | ==Causes of Reporting Bias== | ||