Machine Learning Model Evaluation via Feature Clustering
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Solution Overview
Problem
Machine learning models often operate as black boxes, making it difficult to analyze and correct inaccuracies, leading to inefficient and expensive brute-force training techniques due to the lack of understanding of what causes failures in outputs.
Innovation Solution
A model evaluation system that groups instances from a test dataset based on feature correlations and errors, identifying clusters with higher error concentrations and pinpointing contributing components, allowing for selective refinement and training of specific components rather than the entire system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional brute-force training techniques are used to correct inaccuracies in machine learning models, then the models can be improved, but the process becomes expensive and inefficient
Solution Approach 1:
The patent segments the monolithic training process into distinct phases: evaluation phase (identifying failures and generating failure explanations) and refinement phase (selectively retraining on specific failure clusters). This segmentation allows the system to target only the problematic areas rather than performing exhaustive brute-force training on the entire dataset, thereby improving training efficiency while maintaining model accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where the evaluation system analyzes model failures, generates explanations for why failures occurred, and uses this information to guide selective refinement. The failure explanations provide actionable insights that feed back into the training process, enabling targeted improvements without requiring expensive full-model retraining.
2Adaptability or versatility
If machine learning models operate as black boxes, then they can process complex data, but it becomes difficult to analyze and evaluate failures or inaccuracies
Solution Approach 1:
The patent introduces an intermediary evaluation system that sits between the black-box machine learning model and the user/developer. This evaluation system translates the opaque internal decisions of the model into interpretable failure explanations that describe what caused failures. The intermediary maintains the model's processing capability while making its failures detectable and measurable through natural language explanations.
Solution Approach 2:
The evaluation system enables the machine learning model to self-diagnose its own failures by generating failure explanations that identify what went wrong. This self-service capability allows the model to provide its own evaluation data without requiring external inspection of its internal workings, thus maintaining adaptability while improving failure detectability.
Data Source
AI summary
The present disclosure relates to systems, methods, and computer readable media that evaluate performance of a machine learning system in connection with a test dataset. For example, systems disclosed herein may receive a test dataset and identify label information for the test dataset including feature information and ground truth data. The systems disclosed herein can compare the ground truth data and outputs generated by a machine learning system to evaluate performance of the machine learning system with respect to the test dataset. The systems disclosed herein may further generate feature clusters based on failed outputs and corresponding features and generate a number of performance views that illustrate performance of the machine learning system with respect to clustered groupings of the test dataset.