In machine learning, how do systematic errors typically occur?

Systematic errors in machine learning arise primarily from the assumptions made in algorithms. These assumptions include simplifications or approximations about the underlying data or the problem domain. When an algorithm is designed based on these assumptions, it may not capture the nuances of the data accurately, leading to consistent patterns of error across predictions.

For example, if a model assumes a linear relationship between input and output variables when the actual relationship is more complex, the predictions will generally deviate from the actual values in a consistent manner, resulting in systematic error. Such errors are predictable and can be identified and analyzed to improve the model.

In contrast, other factors like data corruption or random entry mistakes can lead to errors, but these are typically random and do not consistently bias the outcome in the same direction. Using outdated models can also introduce inaccuracies, but this is not necessarily classified as a systematic error; it often relates more to relevance and capability rather than the inherent assumptions of the models themselves.