Bayesian Hierarchical Model Training with Cross-Segment Data
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Solution Overview
Problem
Machine learning models face reliability issues due to small sample sizes and class imbalance in datasets, leading to biased predictions that do not generalize well, especially when high-quality data is scarce.
Innovation Solution
The use of a Bayesian Hierarchical model that generates posterior distributions for parameters based on prior distributions and an updated training dataset, incorporating segment-specific features from similar industries to enhance the training process, allowing for more accurate predictions across different segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If machine learning models are trained using small sample size datasets, then the model can be developed with limited data availability, but the predictions become biased and do not generalize well
Solution Approach 1:
The patent combines data from multiple segments (e.g., different industries or categories) into a unified training dataset. By merging data from similar segments, the model receives sufficient training examples even when individual segments have small sample sizes, thereby improving prediction reliability without requiring large quantities of data for each specific segment
2Reliability
If machine learning models are trained using proxy datasets, then the model can be trained when high-quality data is unavailable, but the model does not capture factor sensitivity of the low-signal population
Solution Approach 1:
The patent applies local quality by training separate models for different segments (e.g., healthcare companies vs. other industries) while sharing common features across segments. This allows each segment to be modeled with its specific characteristics and factor sensitivities, preventing the dilution of segment-specific patterns that occurs when using generic proxy datasets
3Measurement precision
If models are trained segment-specifically, then the model captures factor sensitivity for each population, but the training requires large amounts of high-quality data for each segment
Solution Approach 1:
The patent implements universality by creating a multi-functional training approach where a single model structure serves multiple segments simultaneously. The model shares common features across segments while maintaining segment-specific parameters, allowing one model to handle multiple segments with smaller individual data requirements rather than requiring separate large datasets for each segment
Data Source
AI summary
Methods and systems are described herein for generating a trained Bayesian Hierarchical model from low signal datasets. The disclosed approach utilizes data from alternative segments as a baseline to train the Bayesian Hierarchical model. In some embodiments, the disclosed approach may supplement segment-specific features from another dataset. In some embodiments, inputs for prior distributions may be received from an expert and modified based on the model specification. In one example, the disclosed approach may be used to model probability of default for companies in a low-default segment like Energy portfolio. In this example, data from other commercial and industrial segments is used to form a baseline in the Bayesian Hierarchical model. Further, dataset containing segment-specific features for Energy is supplemented to the training dataset.