Model Interpretability Through Local Feature Path Tracing
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Solution Overview
Problem
The black-box characteristic of complex machine learning models reduces their reliability as users cannot understand the decision-making process and basis, leading to low credibility.
Innovation Solution
A method to generate model interpretability information by determining local feature parameters and paths, using a server to analyze a credit evaluation model, and providing detailed interpretability information to improve understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex machine learning models are used to improve prediction accuracy, then model performance is improved, but model interpretability deteriorates due to black-box characteristics
Solution Approach 1:
The patent segments the complex model's decision-making process into multiple interpretable components including feature contribution analysis, rule extraction, and path tracing. This segmentation allows the model to maintain high prediction accuracy while providing transparent explanations by breaking down the black-box operations into understandable segments that show how inputs lead to outputs through specific feature contributions and decision paths.
2Reliability
If detailed model interpretation information is generated to improve credibility, then interpretability is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-computing and storing feature contribution values, rule templates, and path information during model training or before interpretation is needed. This preliminary preparation allows the system to generate detailed interpretability information quickly when required, improving model credibility without incurring high computational complexity during actual interpretation queries, as the heavy computational work has already been done in advance.
Data Source
AI summary
In a model interpretability information generation method, respective first parameters of a plurality of local features are determined based on the plurality of local features and a target category of a multimedia resource, the target category being obtained through prediction on the multimedia resource by a first model. Respective paths of the plurality of local features are determined based on the plurality of local features and the target category, a starting point of a path of a local feature of the plurality of local features representing the local feature, an end point representing the target category, and the path representing a processing process of obtaining the target category based on the local feature. Based on the respective first parameters and the paths of the plurality of local features, model interpretability information of a process of obtaining the target category by the first model based on the multimedia resource is generated.