Predictor Performance Measurement via Data Slicing
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Solution Overview
Problem
Machine learning predictive models often perform below par on specific data slices within a dataset, leading to unreliable predictions in real-life scenarios due to concealed subpar performance masked by overall good performance across other slices, and existing performance measurement techniques fail to account for slice-specific characteristics.
Innovation Solution
The method involves slicing datasets to compute performance measurements for each slice, allowing for mitigating actions when performance falls below a threshold, such as retraining or replacing hardware, and using domain expert-defined constraints to identify critical data slices for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If overall performance measurement is used to evaluate the predictor, then the evaluation is simple and fast, but the subpar performance on specific data slices is concealed and not detected
Solution Approach 1:
The patent divides the dataset into multiple data slices based on domain expert-defined constraints (e.g., geographic regions, time periods, device types). Instead of evaluating the predictor on the entire dataset as a single unit, the system computes performance measurements for each slice independently. This segmentation enables the detection of subpar performance on specific slices that would be concealed in an overall evaluation, while maintaining computational efficiency by using targeted slicing rather than exhaustive analysis.
2Measurement precision
If data slicing is performed to detect slice-specific performance, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent introduces domain experts as intermediaries who define the constraints used to create data slices. These domain experts provide the specialized knowledge needed to identify meaningful segments of the data without requiring the system to contain embedded domain expertise. The system implements this by allowing domain experts to specify constraints (e.g., 'predictions for heart disease in New York'), which automatically generate the appropriate data slices for evaluation. This intermediary approach maintains measurement precision while avoiding the complexity of hardcoding domain knowledge into the system.
3Reliability
If automated performance measurement with mitigating actions is implemented, then the reliability of predictions improves, but the ease of operation decreases
Solution Approach 1:
The patent implements an automated feedback loop where performance measurements on data slices are continuously monitored and compared against thresholds. When subpar performance is detected on a specific slice, the system automatically triggers mitigating actions such as retraining the predictor with additional data from that slice or adjusting the model parameters. This feedback mechanism improves prediction reliability by ensuring timely responses to performance degradation, while the automation of the feedback process minimizes the operational burden on users compared to manual monitoring and intervention.
Data Source
AI summary
A method, apparatus and a product for determining a performance measurement of predictors. The method comprises obtaining a dataset comprising data instances. Each data instance is associated with a label; obtaining a predictor. The predictor is configured to provide a prediction of a label for a data instance; determining a plurality of data slices that are subsets of the dataset. computing, for each data slice in the plurality of data slices and based on an application of the predictor on each data instance that is mapped to the data slice, a performance measurement that is indicative of a successful label prediction for a data instance comprised by the data slice, whereby obtaining a plurality of performance measurements; based on the plurality of performance measurements, computing a performance measurement of the predictor over the dataset; if the performance measurement of the predictor is below a threshold, performing a mitigating action.