Feasible Performance Region for Model Selection Under Constraints
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Solution Overview
Problem
Predictive model performance assessment is challenging due to misleading accuracy metrics, especially in unbalanced datasets where the cost of false negatives and false positives is mismatched, leading to suboptimal model deployment and resource utilization.
Innovation Solution
Training and assessing multiple models under varying constraints to determine a feasible performance region, which associates each resourcing level with a model, allowing for the selection of the most appropriate model for a given operational constraint and enabling intuitive performance visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single predictive model is trained to maximize overall accuracy, then the model achieves high accuracy on unbalanced datasets, but the model fails to provide reliable predictions for minority classes and misleads non-expert users
Solution Approach 1:
The patent segments the evaluation of model performance by creating multiple specialized models, each trained with different constraints optimized for specific business objectives (e.g., maximizing true positives vs. minimizing false positives). Instead of relying on a single model that averages performance across all scenarios, the system divides the predictive task into multiple specialized models that can be selected based on the specific decision context and cost structure.
Solution Approach 2:
The patent changes the training parameters and constraints of the predictive models to reflect different business scenarios. Each model is trained with specific constraints (e.g., precision constraints, recall constraints, cost-sensitive weights) that align with different business objectives. This allows the system to adapt model behavior to match the specific costs and priorities of each prediction scenario.
2Reliability
If multiple models are trained with different constraints to handle various business scenarios, then the system can provide more reliable predictions for specific objectives, but the system complexity and computational resources increase
Solution Approach 1:
The patent creates a universal framework that can handle multiple business scenarios using a standardized set of tools and procedures. The system uses a common interface for model training, evaluation, and selection that works across different business objectives. The feasible performance region concept provides a universal method for comparing and selecting models regardless of the specific business scenario, reducing the complexity of managing multiple specialized models.
Solution Approach 2:
The patent performs preliminary actions by pre-training multiple models with different constraints before deployment. The feasible performance region is pre-computed to show which models are optimal for different scenarios. This preliminary preparation allows the system to quickly select the appropriate model at runtime without performing complex real-time analysis, reducing operational complexity.
3Ease of operation
If traditional accuracy-based model selection is used, then the model selection process is simple, but the selected model may not be optimal for the specific business scenario and cost structure
Solution Approach 1:
The patent introduces feedback loops where business feedback on prediction outcomes is used to refine model selection. The system learns from actual business results (e.g., which predictions led to correct decisions, what were the actual costs) and uses this feedback to improve future model selections. This feedback mechanism helps non-expert users gradually understand which models work best for their specific scenarios without needing deep expertise.
Solution Approach 2:
The patent introduces an intermediary layer (the feasible performance region and model selection framework) that translates complex model performance characteristics into business-relevant information. This intermediary converts technical model metrics into business value estimates, helping non-expert users make informed decisions about model selection without needing to understand the underlying technical complexities.
Data Source
AI summary
Data characterizing a set of models trained on a dataset using a set of resourcing levels can be received. The set of resourcing levels can specify a condition on outputs of models in the set of models. Performance of the set of models can be assessed using the set of resourcing levels. A feasible performance region can be determined using the assessment. The feasible performance region can associate each constraint in the set of resourcing levels with a model in the set of models. The feasible performance region can be displayed. Related apparatus, systems, articles, and techniques are also described.