Cluster Connectivity Graph for Machine Learning Model Diagnostics
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Solution Overview
Problem
The lack of a universal, fast, and easy-to-use system for model diagnostics in machine learning, particularly for deep neural networks, makes it difficult for both novice and expert users to understand how these models learn and generalize, leading to challenges in identifying performance issues and improving model accuracy.
Innovation Solution
A visualization tool that summarizes the performance of machine learning models by clustering observation vectors, computing accuracy measures, and presenting a cluster connectivity graph to facilitate model improvement and understanding, allowing users to interactively analyze and improve model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used to model system behavior, then model accuracy and ability to learn complicated patterns is improved, but interpretability and understanding of model operation deteriorates
Solution Approach 1:
The patent segments the high-dimensional parameter space into multiple two-dimensional projection planes, each showing different aspects of model behavior. This allows the complex neural network operation to be visualized in manageable 2D views while maintaining accuracy analysis capability.
Solution Approach 2:
The patent transforms the high-dimensional neural network parameter space into multiple 2D projection planes. By creating visual representations in lower dimensions, the system enables human interpretation while preserving the underlying complex relationships through multi-angle projections.
2Adaptability or versatility
If high dimensionality of parameter space is used, then model capability to learn complex patterns is improved, but visualization of model operation deteriorates
Solution Approach 1:
The system projects high-dimensional model parameters onto multiple 2D planes, each representing different feature combinations. This dimensionality reduction enables visual detection and measurement of model behavior while preserving the complex pattern recognition capabilities through selective feature pairing.
Solution Approach 2:
The high-dimensional parameter space is divided into multiple 2D projection planes, each focusing on specific feature pairs. This segmentation makes the visualization task manageable while collectively covering the complex high-dimensional behavior through multiple specialized views.
3Measurement precision
If traditional model diagnostic methods are used, then detailed analysis is possible, but ease of use and understanding for novice users deteriorates
Solution Approach 1:
The diagnostic system segments complex model analysis into multiple 2D projection views, each showing specific aspects of model behavior. This segmentation presents detailed information in manageable, visually intuitive formats that are accessible to users with varying expertise levels.
Solution Approach 2:
The system uses color coding in the visual projections to indicate different performance characteristics and model behaviors. This visual encoding conveys detailed diagnostic information in an easily interpretable format, improving accessibility for novice users while maintaining analytical depth.
Data Source
AI summary
A computing device provides a cluster connectivity graph presented on a display to summarize machine learning model performance. A classification value is predicted is predicted for a response variable value of each observation vector using a trained model. Observation vectors are divided into overlapping data slices that are separately clustered using the predicted classification value to define a set of clusters. A number of observations in each cluster is computed. An accuracy measure is computed for each cluster based on the predicted classification value. A number of overlapping observations between each pair of clusters is computed. The cluster connectivity graph includes a node for each cluster. A size of each node is determined from the computed number of observations. A fill-pattern of each node is determined from the computed accuracy measure. A connector line between each pair of nodes is determined from the computed number of overlapping observations.