Holistic Evaluation Score for Machine Learning Models
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Solution Overview
Problem
Existing machine learning evaluation techniques lack holistic metrics for the complete lifecycle of machine learning systems, are not applicable to graph neural networks or graph training datasets, and fail to provide systematic processes for responsible artificial intelligence metrics, leading to issues like lack of explainability and traceability of model performance degradation.
Innovation Solution
A computer-implemented method generates a holistic evaluation score for machine learning models by aggregating data, model, and decision evaluation scores across various lifecycle stages, applicable to tabular, media, and graph-based datasets, using a pipeline of evaluation techniques to ensure traceability and fairness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional single-aspect evaluation techniques are used, then the evaluation process is simple, but the evaluation comprehensiveness is insufficient
Solution Approach 1:
The patent segments the machine learning model evaluation into three distinct aspects: data evaluation (assessing training dataset quality), model evaluation (assessing performance metrics), and decision evaluation (assessing output class fairness). Each aspect is evaluated separately using specialized techniques, then integrated into a holistic evaluation score. This segmentation allows comprehensive evaluation while maintaining manageable complexity through modular assessment.
Solution Approach 2:
The patent merges multiple evaluation scores from different aspects (data evaluation score, model evaluation score, decision evaluation score) into a single holistic evaluation score. This combining approach provides a comprehensive view of model performance while simplifying the final assessment decision-making process.
2Loss of information
If existing evaluation techniques are used, then the implementation is straightforward, but the traceability to root cause is lacking
Solution Approach 1:
By dividing the evaluation into separate data, model, and decision aspects, the patent enables traceability to specific problem areas. When the holistic evaluation score indicates degradation, practitioners can examine each aspect individually to identify the root cause - whether it lies in data quality, model performance, or decision fairness - rather than dealing with a black-box overall score.
3Adaptability or versatility
If conventional evaluation metrics are used, then the applicability to single model types is maintained, but the universality across different datasets is limited
Solution Approach 1:
The patent creates a universal evaluation framework that can assess machine learning models across different dataset types (tabular, media, text, graph-based) by using aspect-specific evaluation techniques appropriate for each data type. The holistic evaluation score aggregates results from these specialized assessments, providing a unified metric that works universally across diverse datasets while maintaining the nuances needed for each specific type.
Data Source
AI summary
Various embodiments of the present disclosure describe holistic machine learning model evaluation techniques. The techniques include determining a holistic evaluation vector for a target machine learning model based on a plurality of evaluation scores for the target machine learning model. The plurality of evaluation scores may include a data evaluation score corresponding to a training dataset for the target machine learning model, a model evaluation score corresponding to one or more performance metrics for the target machine learning model, and a decision evaluation score corresponding to an output class of the target machine learning model. A holistic evaluation score for the target machine learning model may be determined from the holistic evaluation vector or a plurality of evaluation scores. An informed evaluation output is provided based on the holistic vector or score.