Model Behavior Visualization Using Instance-Feature Variation Clusters
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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 fail to effectively visualize 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 similarities, 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 quality is improved, but interpretability and understanding of input-output relationships deteriorates
Solution Approach 1:
The patent introduces instance-feature variation plots as an intermediary visualization tool that bridges the gap between complex model inputs and outputs. These plots serve as a mediator that translates intricate model behavior into intuitive visual representations, allowing users to understand input-output relationships without directly confronting model complexity
Solution Approach 2:
The patent segments the complex model behavior into individual instance-level analyses. By breaking down the overall model prediction into component parts (individual feature variations for each data instance), the system makes complex model behavior tractable and interpretable at a granular level
2Loss of information
If pure 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 deteriorates
Solution Approach 1:
The patent transitions from one-dimensional numerical information to two-dimensional visual representations. The instance-feature variation plots add a visual dimension to the data, transforming abstract numerical relationships into spatial patterns that reveal nuanced model behavior and exceptional cases that pure numbers cannot convey
3Ease of operation
If data instances are clustered based on instance-feature variation plots, then model behavior understanding is improved, but computational processing time increases
Solution Approach 1:
The patent applies partial action by performing clustering on a subset of instance-feature variation plots rather than all possible data instances. This selective approach provides sufficient insight into model behavior while avoiding the computational burden of comprehensive analysis across the entire dataset
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.