Unified Feature Importance Visualization for Black Box Models
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Solution Overview
Problem
Machine learning models, particularly black box models like neural networks, lack interpretability, making it difficult to understand feature importance and relationships, which is crucial for transparency and trust in predictive outcomes.
Innovation Solution
A method that combines individual conditional expectation (ICE) plots, partial dependence (PDP) plots, and feature importance (PI) plots to provide a comprehensive visual representation of feature importance, allowing for the selection of top features and generation of a final combined plot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If black box machine learning models (neural networks, SVM) are used to achieve high predictive performance, then predictive accuracy is improved, but interpretability and transparency deteriorate
Solution Approach 1:
The patent introduces model-agnostic interpretability tools as intermediaries between black box models and users. These tools (PI, PDP, ICE plots) do not modify the original model but provide intermediate visual representations that bridge the gap between complex predictions and human understanding, allowing users to interpret feature importance without changing the high-performance model structure
Solution Approach 2:
The patent segments the interpretation process into multiple specialized visualization tools, each addressing different aspects: PI plots for overall feature importance, PDP plots for marginal effects, and ICE plots for individual predictions. This segmentation allows comprehensive interpretability while maintaining the integrity of the original high-performance model
2Loss of information
If multiple interpretability tools (PI, PDP, ICE plots) are combined to improve interpretability, then transparency is improved, but complexity and difficulty of visualization increase
Solution Approach 1:
The patent merges multiple separate interpretability tools (PI, PDP, and ICE plots) into a unified visualization framework. By integrating these tools that share common computational foundations and visual representations, the system provides comprehensive interpretability through a single cohesive interface rather than requiring users to separately analyze multiple independent plots
Solution Approach 2:
The patent creates a universal visualization framework that serves multiple interpretability functions simultaneously. The integrated system can display overall feature importance, marginal effects, and individual prediction behaviors within a single unified visualization, allowing one tool to perform the functions of multiple separate tools
Data Source
AI summary
A method, computer system, and a computer program product are provided for post-modeling feature evaluation. In one embodiment, at least at least one post model visual output and associated data is obtained that at least includes an individual conditional expectation (ICE) plot and a partial dependence (PDP) plot. Using the associated data and the plots, a Feature Importance (PI) plot is provided. A plurality of features is then determined for each PI, PDP and ICE plots to calculate at least one Interesting Value for each plot. An overall score is also calculated for each plurality of features based on the associated Interesting Values for each PDP, ICE and PI plots. At least one top feature is selected based on said scores. A final plot is then generated at least reflecting the top feature. The final plot combines the PI, PDP and ICE plots together.