Publication: RiskFix: Supporting Expert Validation of Predictive Timeseries Models in High-Intensity Settings
RiskFix: Supporting Expert Validation of Predictive Timeseries Models in High-Intensity Settings
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Morgenshtern, G., Verma, A., Tonekaboni, S., Greer, R., Bernard, J., Mazwi, M., Goldenberg, A., & Chevalier, F. (2023). RiskFix: Supporting Expert Validation of Predictive Timeseries Models in High-Intensity Settings. EuroVisShort, 13–17. https://doi.org/10.2312/evs.20231036
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Many real-world machine learning workflows exist in longitudinal, interactive machine learning (ML) settings. This longitudinal nature is often due to incremental increasing of data, e.g., in clinical settings, where observations about patients evolve over their care period. Additionally, experts may become a bottleneck in the workflow, as their limited availability, combined with their role as human oracles, often leads to a lack of ground truth data. In such cases where ground truth data is small, the validation of interactive machi
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Morgenshtern, G., Verma, A., Tonekaboni, S., Greer, R., Bernard, J., Mazwi, M., Goldenberg, A., & Chevalier, F. (2023). RiskFix: Supporting Expert Validation of Predictive Timeseries Models in High-Intensity Settings. EuroVisShort, 13–17. https://doi.org/10.2312/evs.20231036
