Interval model calibration with response-consistent supervised learning network

Key Points

• Improved predictive accuracy enhances the overall effectiveness of models from supervised learning.
• The key metric for success involved response consistency across various training datasets throughout the calibration process.
• Analysis of neural network algorithms was applied to optimize interval model calibration.
• These findings indicate that response-consistent approaches may lead to more reliable predictions in practical applications.