GUI Plotting False Positive and Negative Rates for Model Evaluation
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Solution Overview
Problem
In predictive analytics, accuracy is not a reliable metric for characterizing model performance, especially with unbalanced datasets where the cost of false negatives and false positives is mismatched, leading to misleading results for non-expert business users.
Innovation Solution
A graphical user interface (GUI) is developed to visualize and improve model performance by plotting false positive and false negative rates, allowing users to set target accuracy and relative costs of errors, and interactively generating candidate models based on user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If accuracy is used as the primary metric for model performance, then the model development process is simplified, but the results become misleading especially with unbalanced datasets
Solution Approach 1:
The patent segments the single accuracy metric into multiple distinct performance metrics including false positive rate, false negative rate, precision, and recall. This segmentation allows each aspect of model performance to be evaluated independently, providing a more nuanced and accurate characterization of model behavior, especially on unbalanced datasets where different error types have different costs.
Solution Approach 2:
The patent introduces a new dimensional framework by plotting performance metrics on a two-dimensional space with false positive rate on one axis and false negative rate on the other axis. This dimensional transformation moves beyond the single-dimensional accuracy metric, enabling visualization of the trade-off between different types of errors and facilitating more informed model selection based on business context.
2Loss of information
If only accuracy is reported for model performance, then the presentation is concise, but non-expert users cannot understand the real impact of errors
Solution Approach 1:
The patent introduces an intermediary visualization layer (the plot with graphical objects) that translates complex performance metrics into an intuitive visual format. This intermediary representation bridges the gap between technical model performance data and business user understanding, allowing non-experts to grasp the real impact of errors without needing to interpret raw metric values.
Solution Approach 2:
The patent employs color-coded graphical objects and regions in the visualization to encode different performance characteristics and business context. Colors serve as visual cues that immediately convey information about model performance quality and alignment with business objectives, making the interpretation accessible to non-expert users at a glance.
3Loss of information
If the plot includes multiple characterizations of false positive and false negative, then the information completeness is improved, but the visual complexity increases
Solution Approach 1:
The patent merges multiple performance characterizations (false positive rate, false negative rate, precision, recall) into a unified visual representation using graphical objects positioned on a two-dimensional plot. This consolidation allows comprehensive performance information to be displayed simultaneously without creating separate visualizations for each metric, maintaining information completeness while managing visual complexity through integration.
Solution Approach 2:
The graphical objects in the plot serve multiple functions simultaneously: they indicate model performance metrics, show alignment with business context, enable comparison between models, and provide visual feedback for model selection. This multi-functionality reduces the need for separate visual elements for each purpose, achieving information completeness without proportional increases in visual complexity.
Data Source
AI summary
A method includes receiving data characterizing a target accuracy and a performance metric of a model; rendering, within a graphical user interface display space, a plot including a first axis and a second axis, the first axis including a characterization of false positive and the second axis including a characterization of false negative; and rendering, within the graphical user interface display space and within the plot, a graphical object at a location characterizing the performance metric and a visualization indicative of the target accuracy. Related apparatus, systems, techniques and articles are also described.