Automated Slice-Specific Thresholding for ML Model Calibration
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Solution Overview
Problem
The process of setting thresholds for machine learning models is often manual, expensive, prone to errors, and slow, especially when multiple thresholds need to be set for different 'slices' of input data, each with unique constraints and metrics, making it difficult to automate effectively.
Innovation Solution
A method is provided to determine slice-specific thresholds for machine learning models by generating potential threshold values that satisfy constraints and selecting the best ones based on metrics, allowing for automated threshold setting and calibration, reducing computational costs, and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual threshold setting is used to leverage human intuition, then threshold accuracy can be improved, but the process becomes expensive, slow, and difficult to scale
Solution Approach 1:
The system performs self-calibration by automatically determining thresholds through iterative testing and evaluation without human intervention. The model autonomously adjusts thresholds based on performance metrics and constraints, eliminating the need for manual threshold setting while maintaining accuracy.
Solution Approach 2:
The system automatically adjusts threshold parameters based on performance metrics and constraints. By dynamically changing threshold values through automated evaluation and optimization, the system achieves accurate thresholds without manual intervention, resolving the contradiction between accuracy and speed.
2Productivity
If automated threshold setting is implemented, then productivity can be improved, but the system becomes complex and difficult to manage
Solution Approach 1:
The threshold setting process is segmented into distinct automated steps: generating candidate thresholds, evaluating each against constraints, measuring performance metrics, and selecting optimal values. This structured segmentation simplifies the automation process while maintaining high productivity.
Solution Approach 2:
The system incorporates feedback loops where threshold candidates are evaluated against constraints and metrics, and results feed back into the selection process. This automated feedback mechanism manages system complexity by providing clear guidance for threshold optimization without manual intervention.
3Measurement precision
If multiple slice-specific thresholds are determined manually, then measurement precision can be improved, but the time and computational resources required increase significantly
Solution Approach 1:
The input data is segmented into different slices, and the system automatically determines thresholds for each slice independently through streamlined processes. This segmentation enables precise slice-specific thresholds without proportionally increasing time consumption, as automation handles multiple slices efficiently in parallel or sequential batches.
Data Source
AI summary
Methods and systems are provided for generating, for respective mutually exclusive classes of model inputs, separate output thresholds that can be applied to the continuous-valued output of a neural network or other machine learning model in order to classify inputs in a class-sensitive manner. Such classes could be related to operational or other constraints with respect to the classifier outputs that vary across the classes of inputs. Thus, the machine learning model can be improved by using training data from all of the available classes while allowing the end performance of the model plus threshold classifier to be separately set for each input class. These automated methods for class-specific threshold setting also provide improvements with respect to accuracy, time, and cost. Also provided are methods and systems for per-class calibration of model outputs.