# Comparing Shape-Constrained Regression Algorithms for Data Validation

arXiv: https://arxiv.org/abs/2209.09602 · HTML: https://mlsanity.com/paper/2209.09602/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 20 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Shape-constrained Symbolic Regression -- Improving Extrapolation with Prior Knowledge](https://mlsanity.com/paper/2103.15624/) (2021)
- [A Survey of Data Quality Measurement and Monitoring Tools](https://mlsanity.com/paper/1907.08138/) (2019)
- [Optimization over Nonnegative and Convex Polynomials With and Without Semidefinite Programming](https://mlsanity.com/paper/1806.06996/) (2018)

## Abstract (arXiv)

Industrial and scientific applications handle large volumes of data that render manual validation by humans infeasible. Therefore, we require automated data validation approaches that are able to consider the prior knowledge of domain experts to produce dependable, trustworthy assessments of data quality. Prior knowledge is often available as rules that describe interactions of inputs with regard to the target e.g. the target must be monotonically decreasing and convex over increasing input values. Domain experts are able to validate multiple such interactions at a glance. However, existing rule-based data validation approaches are unable to consider these constraints. In this work, we compare different shape-constrained regression algorithms for the purpose of data validation based on their classification accuracy and runtime performance.