AI Model Validation via Synthetic Dependent Variable Simulation
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Solution Overview
Problem
Supervised machine learning models trained for one population cannot be effectively evaluated for their efficacy in predicting outcomes for a different population with distinct characteristics, as conventional methods lack the necessary dependent variable information for the new environment.
Innovation Solution
A system comprising a model server, resource management server, feature data store, and model data store, where the model server processes feature vectors from both populations to generate risk predictions and simulated test data, and the resource management server determines the validity of the model by comparing these predictions with simulated test data using a root mean square error (RMSE) value against a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a machine learning model trained for a first population is applied to a second population with different characteristics, then the model can be used in the new environment, but the model's efficacy cannot be evaluated by conventional methods due to lack of dependent variable information
Solution Approach 1:
The patent creates a synthetic copy of the target population's dependent variable data through simulation. A simulation model is trained on available data and then used to generate synthetic outcome data that mimics the characteristics of the target population. This simulated data serves as a proxy for the actual dependent variable information that would otherwise be unavailable, enabling model evaluation without requiring real outcome data from the new population.
Solution Approach 2:
The patent introduces a simulation model as an intermediary between the predictive model and the evaluation process. This simulation model acts as a mediator that generates synthetic dependent variable data, allowing the evaluation of the predictive model's performance on the target population without direct access to real outcome data. The intermediary simulation bridge enables indirect validation of the model's efficacy.
2Reliability
If simulated test data is generated to evaluate the model, then model validation becomes possible, but the system complexity increases with multiple models and data processing steps
Solution Approach 1:
The patent creates a multi-functional evaluation system where the simulation model serves multiple purposes: it generates synthetic training data, creates test data for validation, and provides a consistent framework for evaluating model performance across different populations. This universal approach consolidates multiple functions into a single coherent system, managing complexity through functional integration rather than proliferation of separate components.
Solution Approach 2:
The patent performs preliminary simulation and validation activities before deploying the predictive model in the new environment. The simulation model is trained and validated in advance, and synthetic data is generated beforehand to establish evaluation metrics. This preliminary action allows thorough model validation to occur before real-world deployment, reducing the need for complex post-deployment adjustments and validation procedures.
Data Source
AI summary
Systems and methods are described which relate to machine learning model validation. A first machine learning model may be trained to dependent variable data for a first population. A second machine learning model may be trained to simulate dependent variable data for the first population. The second machine learning model may then be applied to student activity data of a second population having different characteristics from the first population to produce simulated dependent variable data. The first machine learning model may then generate predictions for the second population, which may be validated via comparison to the simulated dependent variable data. A given simulated dependent variable value may be generated by the second machine learning model at a specific time TX, where some features input to the machine learning model may be derived from datapoints occurring before TX and others being derived from datapoints occurring after TX.