Multi-Model Simulation Framework for Holistic ML Predictions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing simulation analysis technologies, such as probabilistic structural equation models (PSEMs), are limited in their predictive capability due to the inability to generate simultaneous simulations and predictions across multiple models, failing to capture holistic, multi-parameter interactions and real-world scenarios.
Innovation Solution
A multiple-model machine learning architecture integrates tiers of individual predictive models, addressing collinearity by determining variable effects and generating combined prediction outputs that capture data feature behaviors and relationships across various tiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If multiple independent PSEMs are used to analyze different quadrants, then each model can be trained separately, but the ability to simultaneously generate simulations and predictions across multiple models is lost
Solution Approach 1:
The patent combines multiple independent PSEMs into a unified framework that allows simultaneous simulation and prediction across all models. The system integrates separate quadrant analyses while maintaining the ability to generate holistic predictions by merging the predictive outputs of individual models through a coordinated simulation engine.
Solution Approach 2:
The patent creates a universal simulation framework that can handle multiple PSEMs and different analysis quadrants through a single system. This multi-functional approach enables the same infrastructure to perform diverse predictive tasks across different models simultaneously, rather than requiring separate dedicated systems for each quadrant.
2Device complexity
If traditional separate model analysis is used, then model complexity is reduced, but the ability to capture holistic multi-parameter interactions is limited
Solution Approach 1:
The patent implements a nested architecture where individual PSEM models are embedded within a larger integrated simulation framework. Each quadrant model maintains its own structure and parameters while being nested within the comprehensive system that enables cross-model simulations and captures holistic interactions between different parameter sets.
3Power
If independent models are used for each quadrant, then computational requirements per model are reduced, but the overall computational operations and data requirements increase
Solution Approach 1:
The patent enables continuous simultaneous operation of multiple PSEMs through the integrated framework, allowing all models to generate predictions and simulations concurrently rather than sequentially. This continuous parallel processing maintains lower computational requirements per model while significantly improving overall productivity through coordinated multi-model output.
Data Source
AI summary
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for integrating traditionally disparate machine learning models by determining a plurality of changes to a first variable based on a plurality of changes to one or more second variables, determining estimated effects in a first variable model based on an optimization of the one or more second variables, generating a plurality of first prediction outputs based on the estimated effects, and generating a set of combined prediction outputs by combining the plurality of first prediction outputs with a plurality of second prediction outputs that is associated with estimated effects in the one or more second variables.