Visual Representation of Machine Learning Model Performance
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Solution Overview
Problem
End-users, especially those without programming or computer science skills, struggle to monitor and evaluate the performance of machine learning models, including identifying anomalies and data drift in training datasets.
Innovation Solution
The development of systems and methods that provide graphical user interfaces (GUIs) to visually represent how data is treated by machine learning models, allowing users to identify anomalies, track data drift, and gain insights into the ingested training data and predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning models are deployed for data analysis and prediction, then the model's analytical capability and automation level are improved, but the difficulty of monitoring and evaluating model performance increases for end-users
Solution Approach 1:
The patent introduces an intermediary visualization system that sits between the machine learning model and the end-user. This system translates complex model operations into intuitive visual representations, allowing users to monitor model performance without needing to understand the underlying complex algorithms or write code.
Solution Approach 2:
The patent replaces the mechanical approach of code-based monitoring with a visual interface system. Instead of requiring users to write and execute monitoring code, the system automatically generates visual representations of model performance, substituting the manual coding process with an automated visualization mechanism.
2Measurement precision
If detailed code-based monitoring solutions are provided, then the measurement precision of model performance is improved, but the device complexity and programming requirements increase
Solution Approach 1:
The patent creates visual copies or representations of the model's internal operations and data transformations. Instead of directly exposing complex code and algorithms, the system generates simplified visual copies that represent the same information in an accessible format, maintaining measurement precision while reducing complexity.
Solution Approach 2:
The patent segments the complex model performance monitoring task into multiple visual components displayed through GUI elements. Each visual element represents a specific aspect of model performance, breaking down the overall complex system into manageable, independently understandable visual segments.
3Ease of operation
If visual representation systems are implemented, then the ease of operation for end-users is improved, but the processing overhead and system complexity increase
Solution Approach 1:
The patent implements a universal visualization system that can represent multiple types of model operations and performance metrics through a single integrated GUI framework. This multi-functional system handles various visualization needs (data drift detection, anomaly identification, performance tracking) through consistent visual patterns, reducing the need for separate specialized systems.
Data Source
AI summary
Disclosed herein at methods and systems for visualizing machine learning model performance. One method comprises receiving a request to provide a visual representation of a machine learning technique executed on a set of images to generate a first attribute and a second attribute for each image; executing the machine learning model to receive the first and the second attribute for each image; mapping the first attribute to a visual distinctiveness protocol; identifying a distance for each image, the distance representing a difference between the second attribute predicted by the model for each pair of respective images within the set of images; and providing for display at least a subset of the set of images arranged in accordance with their respective distance and having a visual attribute corresponding to the mapped first attribute for each image.