Instance-Feature Variation Plots for Complex Model Interpretability
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Solution Overview
Problem
Complex computer models are difficult to interpret due to their high complexity, making it challenging to understand the relationship between model inputs and outputs, especially in different regions of the input space, and existing methods are inadequate for visualizing nuanced model behavior across varying feature spaces.
Innovation Solution
A model visualization system that generates instance-feature variation plots to analyze how modifying individual data instances affect model outputs, clusters data instances based on these plots, and uses k-means clustering to identify similar behaviors, with additional features identified through shallow decision trees to explain cluster membership.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the complexity of computer models is increased to improve learning objectives, then model output accuracy is improved, but interpretability of the relationship between inputs and outputs deteriorates
Solution Approach 1:
The patent introduces instance-feature variation plots as an intermediary visualization tool that bridges the gap between complex model internals and human understanding. These plots serve as a mediator that translates complex model behavior into intuitive visual representations showing how individual instances respond to feature variations, enabling users to interpret model predictions without understanding the underlying complex computations
Solution Approach 2:
The patent segments the complex model behavior into individual instance-level analyses. Instead of attempting to understand the entire model at once, the system breaks down model predictions into discrete instance-feature variations, allowing users to examine and interpret each instance's response to feature changes independently, thereby making the overall complex model behavior more comprehensible
2Productivity
If the number of layers and processing functions in a model is increased, then model performance is improved, but ease of understanding model behavior deteriorates
Solution Approach 1:
The visualization system acts as an intermediary layer between the complex multi-layered model and the user. Instead of requiring users to directly understand complex processing functions across multiple layers, the system introduces instance-feature variation plots that mediate this understanding by showing the net effect of all layers on individual predictions in an intuitive visual format
Solution Approach 2:
The patent creates simplified copies or representations of complex model behavior through visual plots. These instance-feature variation plots are simplified visual copies that capture the essential input-output relationships without replicating the full complexity of the multi-layered processing, making model behavior easier to understand while preserving the underlying performance
3Loss of information
If numerical or data-based information is used to explain model behavior, then some information is provided, but nuanced or complex model behavior in uncommon cases cannot be effectively explained
Solution Approach 1:
The patent employs visual representations with varying colors and patterns to encode different aspects of model behavior. These visual changes allow nuanced differences in model predictions across different regions of the input space to be detected and understood, transforming abstract numerical relationships into perceptible visual patterns that reveal complex behavior
Solution Approach 2:
The system transforms one-dimensional numerical model outputs into two-dimensional visual plots that add the dimension of feature variation. This dimensional transformation allows users to simultaneously observe how model predictions change across different feature values and different instances, revealing nuanced patterns and relationships that would be difficult to detect in purely numerical data
Data Source
AI summary
A model visualization system analyzes model behavior to identify clusters of data instances with similar behavior. For a selected feature, data instances are modified to set the selected feature to different values evaluated by a model to determine corresponding model outputs. The feature values and outputs may be visualized in an instance-feature variation plot. The instance-feature variation plots for the different data instances may be clustered to identify latent differences in behavior of the model with respect to different data instances when varying the selected feature. The number of clusters for the clustering may be automatically determined, and the clusters may be further explored by identifying another feature which may explain the different behavior of the model for the clusters, or by identifying outlier data instances in the clusters.