This repository provides the supporting code for the Monte Carlo Bounds for Reasonable Predictions (MC-BRP) algorithm described in Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting, which won the Best Student Paper Award (CS) at the ACM conference on Fairness, Accountabillity, and Transparency in 2020: https://arxiv.org/abs/1908.00085.
Since the dataset used in the paper is private, we provide an analogous dataset and model about predicting the critical temperature of molecular compunds from the UCI machine learning repository: https://archive.ics.uci.edu/ml/datasets/Superconductivty+Data. MC-BRP is applicable to any regression task with numeric features. It can also be applied to classification tasks where all errors are defined as large errors.