# An Efficient Model-Agnostic Approach for Uncertainty Estimation in Data-Restricted Pedometric Applications

arXiv: https://arxiv.org/abs/2409.11985 · HTML: https://mlsanity.com/paper/2409.11985/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 42 references that are in the map.
- First posted on arXiv 18 Sep 2024.

## Builds on (most cited first, 16 of 16)

- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://mlsanity.com/paper/2207.01848/) (2022)
- [Transformers Can Do Bayesian Inference](https://mlsanity.com/paper/2112.10510/) (2021)
- [When Do Neural Nets Outperform Boosted Trees on Tabular Data?](https://mlsanity.com/paper/2305.02997/) (2023)
- [Stop Regressing: Training Value Functions via Classification for Scalable Deep RL](https://mlsanity.com/paper/2403.03950/) (2024)
- [LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images](https://mlsanity.com/paper/1803.00455/) (2018)
- [Improving Deep Regression with Ordinal Entropy](https://mlsanity.com/paper/2301.08915/) (2023)
- [On the Reliable Detection of Concept Drift from Streaming Unlabeled Data](https://mlsanity.com/paper/1704.00023/) (2017)
- [Regression as Classification: Influence of Task Formulation on Neural Network Features](https://mlsanity.com/paper/2211.05641/) (2022)
- [Deep Distribution Regression](https://mlsanity.com/paper/1903.06023/) (2019)
- [A step towards understanding why classification helps regression](https://mlsanity.com/paper/2308.10603/) (2023)
- [Interpretable Machine Learning for TabPFN](https://mlsanity.com/paper/2403.10923/) (2024)

## Built on by (most cited first, 3 of 3)

- [Modern Neural Networks for Small Tabular Datasets: The New Default for Field-Scale Digital Soil Mapping?](https://mlsanity.com/paper/2508.09888/) (2025)
- [LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping](https://mlsanity.com/paper/2502.20139/) (2025)
- [Kriging prior Regression: A Case for Kriging-Based Spatial Features with TabPFN in Soil Mapping](https://mlsanity.com/paper/2509.09408/) (2025)

## Abstract (arXiv)

This paper introduces a model-agnostic approach designed to enhance uncertainty estimation in the predictive modeling of soil properties, a crucial factor for advancing pedometrics and the practice of digital soil mapping. For addressing the typical challenge of data scarcity in soil studies, we present an improved technique for uncertainty estimation. This method is based on the transformation of regression tasks into classification problems, which not only allows for the production of reliable uncertainty estimates but also enables the application of established machine learning algorithms with competitive performance that have not yet been utilized in pedometrics. Empirical results from datasets collected from two German agricultural fields showcase the practical application of the proposed methodology. Our results and findings suggest that the proposed approach has the potential to provide better uncertainty estimation than the models commonly used in pedometrics.