# A Characterization of Prediction Errors

arXiv: https://arxiv.org/abs/1611.05955 · HTML: https://mlsanity.com/paper/1611.05955/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2016.

## Built on by (most cited first, 2 of 2)

- [Shared Interest: Measuring Human-AI Alignment to Identify Recurring Patterns in Model Behavior](https://mlsanity.com/paper/2107.09234/) (2021)
- [Generalizable Error Modeling for Human Data Annotation: Evidence From an Industry-Scale Search Data Annotation Program](https://mlsanity.com/paper/2310.05286/) (2023)

## Abstract (arXiv)

Understanding prediction errors and determining how to fix them is critical to building effective predictive systems. In this paper, we delineate four types of prediction errors and demonstrate that these four types characterize all prediction errors. In addition, we describe potential remedies and tools that can be used to reduce the uncertainty when trying to determine the source of a prediction error and when trying to take action to remove a prediction errors.