Classification Model Probability Calibration for Reliable Evaluation
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Solution Overview
Problem
The lack of a universally agreed-upon metric for evaluating the quality of trained machine learning models leads to subjective user feedback, as stakeholders often lack clear context and cost/return information, affecting user confidence and adoption.
Innovation Solution
A system that enhances machine learning model predictions by calibrating probabilities, balancing training data sets, and respecting expected class groupings, using model configuration data to improve prediction accuracy and user-friendly output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective user feedback is used to evaluate model quality, then user perspective is captured, but measurement precision is poor
Solution Approach 1:
The patent transforms the evaluation from subjective user feedback to objective quantitative metrics by changing the parameters used for assessment. It introduces specific measurable parameters including calibration quality (how well predicted probabilities match actual outcomes), balanced accuracy (performance across different data distribution scenarios), and class ordering quality (whether predicted class sequences match domain expectations). This parameter transformation enables precise, objective model quality evaluation without relying on subjective user opinions.
2Manufacturing precision
If model predictions are enhanced with calibration and balancing, then prediction accuracy improves, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary evaluation system that acts as a mediator between the original model predictions and the final enhanced predictions. This intermediary layer implements three specific enhancement mechanisms: calibration adjustment (modifying probability outputs to match empirical frequencies), balancing adjustment (correcting for class distribution biases), and class ordering adjustment (ensuring predicted class sequences align with domain knowledge). By structuring the enhancement as a separate intermediary system with clear modular components, the patent improves prediction accuracy while managing complexity through organized architecture.
Solution Approach 2:
The patent segments the prediction enhancement process into three distinct, independent modules: calibration quality enhancement, balanced accuracy enhancement, and class ordering quality enhancement. Each module addresses a specific aspect of prediction quality independently, allowing them to be developed, tested, and applied separately. This segmentation reduces overall system complexity by breaking down the complex enhancement task into manageable, focused components that can be independently optimized and maintained.
Data Source
AI summary
Systems and methods provide reception of an identifier of a machine learning classification model and a prediction generated by the machine learning classification model, identification of model configuration data associated with the machine learning classification model, modification of the prediction based on the model configuration data to generate an enhanced prediction comprising calibrated probabilities, and returning of the enhanced prediction.