Correcting sampling biases via importance reweighting for spatial modeling
This addresses distribution shift issues in spatial modeling for fields like environmental studies, but it is incremental as it builds on existing importance sampling techniques.
The paper tackled the problem of distribution bias in spatial data by introducing an importance reweighting method to obtain unbiased error estimates, reducing overall prediction error from 7% to 2% with improvements for larger samples.
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.