# Correcting sampling biases via importance reweighting for spatial modeling

arXiv: https://arxiv.org/abs/2309.04824 · HTML: https://mlsanity.com/paper/2309.04824/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 9 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [Uncertainty in Gradient Boosting via Ensembles](https://mlsanity.com/paper/2006.10562/) (2020)
- [Predicting into unknown space? Estimating the area of applicability of spatial prediction models](https://mlsanity.com/paper/2005.07939/) (2020)
- [Uncertainty Estimation of Transformers' Predictions via Topological Analysis of the Attention Matrices](https://mlsanity.com/paper/2308.11295/) (2023)
- [Recurrent Convolutional Neural Networks help to predict location of Earthquakes](https://mlsanity.com/paper/2004.09140/) (2020)
- [Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust](https://mlsanity.com/paper/2302.02834/) (2023)
- [Optimal soil sampling design based on the maxvol algorithm](https://mlsanity.com/paper/2103.10337/) (2021)
- [Predicting spatial distribution of Palmer Drought Severity Index](https://mlsanity.com/paper/2208.14833/) (2022)

## Abstract (arXiv)

In machine learning models, the estimation of errors is often complex due to distribution bias, particularly in spatial data such as those found in environmental studies. We introduce an approach based on the ideas of importance sampling to obtain an unbiased estimate of the target error. By taking into account difference between desirable error and available data, our method reweights errors at each sample point and neutralizes the shift. Importance sampling technique and kernel density estimation were used for reweighteing. We validate the effectiveness of our approach using artificial data that resemble real-world spatial datasets. Our findings demonstrate advantages of the proposed approach for the estimation of the target error, offering a solution to a distribution shift problem. Overall error of predictions dropped from 7% to just 2% and it gets smaller for larger samples.