Model Prediction Explanation Using Dynamic Symmetric Gradient Paths
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Solution Overview
Problem
Existing machine learning model explanation methods, particularly black-box approaches like LIME and SHAP, face challenges in achieving accurate and efficient feature contribution estimation due to the need to explore all possible paths, leading to high computational complexity and potential bias.
Innovation Solution
A dynamic gradient estimation method (DyGEst) that calculates feature contributions using symmetric paths and dynamically adds intermediary points until a desired accuracy threshold is reached, reducing computational load by selecting a subset of symmetric permutations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all possible paths are explored to calculate feature contributions, then measurement precision of prediction explanation is improved, but device complexity and computational power requirements worsen
Solution Approach 1:
The method segments the feature space exploration by dividing it into multiple symmetric paths from a reference point to the target point. Instead of exhaustively exploring all possible paths, it segments the exploration into a manageable number of symmetric trajectories, each contributing to the overall feature contribution estimation. This segmentation reduces computational complexity while maintaining explanation accuracy.
Solution Approach 2:
The method applies local quality by focusing computational resources on specific symmetric paths that are most informative for feature contribution estimation. Rather than uniformly exploring all paths, it concentrates on paths that provide local insights into feature contributions, optimizing the trade-off between computational effort and explanation precision.
2Measurement precision
If all possible paths are explored to calculate feature contributions, then measurement precision of prediction explanation is improved, but productivity and computational time worsen
Solution Approach 1:
The method segments the computationally intensive path exploration task into multiple independent symmetric path calculations. Each path can be computed separately and efficiently, allowing for parallel processing and improving overall productivity. The segmentation enables the system to achieve accurate prediction explanations without the prohibitive computational cost of exhaustive path exploration.
Solution Approach 2:
The method employs partial action by calculating feature contributions along a selected set of symmetric paths rather than all possible paths. This partial exploration is sufficient to achieve the desired measurement precision while significantly improving computational efficiency and productivity compared to exhaustive exploration.
3Measurement precision
If sophisticated metrics are used for prediction explanation, then measurement precision is improved, but interpretability worsens
Solution Approach 1:
The method uses symmetric paths to provide locally informative feature contribution estimates that are both precise and interpretable. Each symmetric path provides localized insights into how features contribute to predictions, making the explanation both accurate and understandable to users without requiring sophisticated metric knowledge.
Data Source
AI summary
A method provides an accurate machine learning model output explanation. A symmetric path from a reference point to an input data point is determined. The symmetric path represents contributions from features in a feature space of a prediction model. The contribution of the intercept is calculated as the machine learning model output value at the reference point in the feature space. The symmetric path is updated based on the calculated contributions. An encircling path set is generated from the updated base path based on the desired accuracy threshold. A gradient is calculated for each path. Intermediary points between the reference point and the input data point are dynamically added. The added points are symmetric about the symmetric path. A determination of whether a threshold accuracy of prediction is produced by the prediction model is made. The dynamic addition of intermediary points is iterated until the threshold accuracy is reached.