Internal covariate shift occurs when the distribution of features changes over time within the same concept. This can lead to a performance degradation of a machine learning model as it becomes less effective at capturing the underlying relationship between features and the concept of interest. Data preprocessing techniques such as normalization, standardization, and feature engineering can help mitigate the effects of internal covariate shift by transforming the data into a form that is more stable and easier for the model to learn from.
