ML Behavioral Requirement Policies for Use-Case Model Evaluation
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Solution Overview
Problem
Current communication systems lack a means for analytics consumers to provide customized behavioral requirements for machine learning models, such as false positives, false negatives, and accuracy, during training, and for analytics producers to choose appropriate performance evaluation metrics based on use cases.
Innovation Solution
An apparatus and method for determining behavioral requirement policies for machine learning models, allowing analytics consumers to provide specific requirements and analytics producers to determine and report performance evaluation metrics, such as precision, recall, and error metrics, to ensure model training meets these criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained without customized behavioral requirement policies, then the training process is simple and fast, but the model performance cannot be optimized for specific use cases
Solution Approach 1:
The patent introduces behavioral requirement policies as a set of configurable parameters that define model training objectives. These policies include parameters such as false positive tolerance, false negative tolerance, and accuracy requirements. By changing these policy parameters, the training process can be adapted to different use cases without fundamentally changing the training architecture, thus improving model performance while maintaining manageable complexity.
Solution Approach 2:
The patent implements a feedback mechanism where analytics consumers specify behavioral requirements, analytics producers train models accordingly, and performance evaluation metrics are used to verify compliance. This closed-loop feedback ensures that models are trained to meet specific use case requirements while providing continuous improvement opportunities.
2Adaptability or versatility
If analytics consumers provide customized behavioral requirements, then model training can be optimized for specific use cases, but the system complexity increases
Solution Approach 1:
The patent creates a universal framework where analytics consumers can specify various behavioral requirements (false positive tolerance, false negative tolerance, accuracy) that work across different use cases. The same policy specification mechanism serves multiple purposes: classification tasks, regression tasks, and different domain applications. This multi-functionality enables high adaptability without proportionally increasing system complexity.
Solution Approach 2:
The patent segments the behavioral requirements into distinct policy components (false positive policy, false negative policy, accuracy policy). Each component can be independently configured and optimized for specific use cases. This segmentation allows flexible adaptation to different scenarios while keeping the overall system structure modular and manageable.
3Measurement precision
If performance evaluation metrics are determined based on behavioral requirement policies, then model accuracy improves, but the evaluation process becomes more complex
Solution Approach 1:
The patent implements self-service evaluation where the system automatically determines performance evaluation metrics based on the provided behavioral requirement policies. The analytics producer selects appropriate metrics (precision, recall, F1-score, accuracy) and computes them automatically during model evaluation. This eliminates the need for manual metric selection and complex evaluation procedures, thereby improving measurement precision while maintaining simple evaluation processes.
Data Source
AI summary
An apparatus is disclosed, said apparatus comprising means for determining, for a given use case, a behavioural requirement policy for a machine learning model, means for providing an indication of the behavioural requirement policy to an analytics producer and means for receiving, from the analytics producer, a performance evaluation metric determined based on the behavioural requirement policy.