Parameter Evaluation for Interpretable Machine Learning Models
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Solution Overview
Problem
Automated machine learning solutions, such as equations derived from data sets, often obscure the relative contribution of parameters to outcomes, making it difficult for humans to understand and optimize these parameters effectively.
Innovation Solution
The implementation uses genetic algorithms to evaluate and normalize coefficients of parameters associated with outcomes, allowing for the ranking of parameters by their contribution, thereby providing insight into which parameters have the most influence on the outcomes, as seen in the context of web page load times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated machine learning techniques are used to determine solutions based on data sets, then the accuracy and effectiveness of the solution are improved, but the understandability and interpretability of parameter contributions deteriorate
Solution Approach 1:
The patent introduces an intermediary analysis layer that processes the black-box machine learning model outputs. This intermediary component decomposes the model's decision-making process into attributable parameter contributions, allowing users to understand which parameters most influence outcomes without sacrificing the model's predictive accuracy.
Solution Approach 2:
The system dynamically changes and analyzes parameter values to determine their marginal contribution to outcomes. By systematically varying parameters and measuring the resulting changes in model predictions, the system quantifies each parameter's influence, transforming the opaque model into an interpretable framework that maintains high accuracy.
2Measurement precision
If complex machine learning models are applied to evaluate web page parameters, then the precision of load time prediction is improved, but the complexity of the evaluation system increases
Solution Approach 1:
The patent segments the complex machine learning evaluation process into distinct, manageable components: data collection, model training, parameter attribution analysis, and result visualization. Each component operates independently and can be optimized or replaced without affecting the entire system, thereby reducing overall complexity while maintaining prediction precision.
Solution Approach 2:
The system implements feedback mechanisms that automatically adjust the evaluation process based on performance metrics. When prediction precision is sufficient, the system can simplify its operation; when higher accuracy is needed, it automatically increases computational resources. This feedback-driven approach balances precision requirements with system complexity management.
Data Source
AI summary
The described implementations relate to machine learning. One implementation provides a technique involving logging data that includes outcomes and values of first and second parameters that are associated with the outcomes. The technique can also include determining an equation that includes a first coefficient for the first parameter and a second coefficient for the second parameter, normalizing the first coefficient based on the values of the first parameter, and normalizing the second coefficient based on the values of the second parameter. The first parameter and the second parameter can be ranked in order of contribution to the outcomes based on the normalized first and second coefficients.