Drift Attribution Model for Metric Factor Quantification
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Solution Overview
Problem
Conventional approaches to evaluating metric drift in complex systems, such as machine learning models, rely on human expertise, which is prone to errors and subjective interpretations, and struggle to scale across diverse domains, limiting the ability to objectively attribute changes in metrics to underlying factors.
Innovation Solution
A trained drift attribution model, combined with a Shapely explainer, generates a metric drift report that objectively quantifies the impact of each factor on a metric by learning relationships between factors and their effects through a baseline and perturbed dataset training process, enabling automated and scalable analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional approaches relying on human expertise are used to evaluate metric drift, then subjective interpretation and human error occur, but automation and objectivity are reduced
Solution Approach 1:
The system enables automated self-evaluation of metric drift by training a drift attribution model that automatically processes input data, computes factor contributions using Shapley values, and generates drift reports without requiring human intervention in the evaluation process
Solution Approach 2:
The patent replaces human expert analysis with a computational model that uses machine learning algorithms (gradient boosting decision trees) and mathematical methods (Shapley values) to objectively attribute metric drift to underlying factors, substituting mechanical human judgment with automated computational analysis
2Productivity
If manual evaluation methods are used for metric drift analysis, then scalability across diverse domains is limited, but complexity of implementation is reduced
Solution Approach 1:
The drift attribution model is designed as a universal system that can evaluate metrics across diverse domains by accepting generic input data formats and producing standardized drift reports, enabling the same model to handle different types of metrics and factors through configuration rather than domain-specific customization
Solution Approach 2:
The system segments the complex task of metric drift analysis into distinct computational components: data preprocessing, factor contribution computation using Shapley values, drift magnitude calculation, and report generation, allowing each component to be independently optimized and maintained
3Loss of information
If comprehensive analysis of multiple factors is performed, then understanding of metric changes improves, but computational complexity increases
Solution Approach 1:
The patent introduces Shapley values as an intermediary mathematical framework that systematically distributes the contribution of multiple factors to metric drift, providing a rigorous method to apportion responsibility across all factors while maintaining computational tractability through established algorithms
Data Source
AI summary
Multi-factor metric drift evaluation and attribution techniques are described. A drift attribution model is trained to compute, for a segment of input data that defines an observed value for a metric and observed values for each of a plurality of factors that influence the value of the metric, a contribution by each of the plurality of factors to the observed metric value. Drift observations output by the trained drift attribution model are further processed using a Shapely explainer to represent contributions of each of the metric factors, and their associated values, relative to one or more observed values of a metric during the time segment. The respective magnitude by which each metric factor affects an observed value of the metric is described in a metric drift report, which objectively quantifies respective impacts of a factor, relative to other factors that affect a metric.