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

Correcting sampling biases via importance reweighting for spatial modeling