Dataset Evaluation System for ML Training Data Variance
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Solution Overview
Problem
Machine learning models often exhibit unwanted behavior due to incorrect or malicious data during the training process, which can be difficult to detect and may result from biased or erroneous annotations, leading to misclassification and noisy outputs.
Innovation Solution
A method and system for evaluating datasets by calculating a baseline of variation for an existing dataset and comparing it with the new dataset's output value, using a data pre-processor, machine learning model, model tester, fairness analyzer, and distributed ledger to determine if the variance is within an acceptable range, thereby preventing the integration of unwanted data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If new datasets are acquired for integration with existing training data, then the quantity and diversity of training data increases, but the risk of introducing unwanted or malicious data increases
Solution Approach 1:
The system performs preliminary evaluation of new datasets before integration by calculating baseline statistics from existing trusted data and comparing them against the new dataset's characteristics. This advance detection mechanism identifies unwanted data patterns before they can contaminate the training corpus, allowing rejection or cleaning of problematic datasets while still enabling integration of safe, high-quality data.
2Measurement precision
If manual annotation is performed to improve data accuracy, then labeling precision increases, but the time and cost required for data preparation increases
Solution Approach 1:
The system enables self-service data validation by automatically calculating baseline statistics from existing annotated data and using these baselines to evaluate new datasets. This automated statistical comparison replaces or reduces the need for time-consuming manual verification of each new dataset, allowing rapid assessment of data quality while maintaining accuracy standards through programmatic baseline comparison.
3Reliability
If statistical baseline comparison is performed on all new datasets, then data quality control improves, but the computational processing time increases
Solution Approach 1:
The system changes parameters by focusing statistical comparison on specific baseline metrics (mean, standard deviation, distribution characteristics) rather than exhaustive analysis of all data attributes. This selective parameter comparison maintains reliable quality control by monitoring key statistical indicators while reducing overall processing time through targeted rather than comprehensive evaluation.
Data Source
AI summary
A method and a system for dataset evaluation are provided. The method can include acquiring a new dataset for integration with an existing dataset. The existing dataset is used to train a machine learning model. The method can also include calculating a baseline of variation for the existing dataset. The baseline of variation can be determined by evaluating various characteristics of the existing dataset. The method can further include determining an output value for the new dataset by also evaluating characteristics of the new dataset. The method can also include comparing the output value to the baseline of variation to generate a variance between the output value and the baseline of variation. The method can further include determining whether the variance is within an acceptable range of the baseline of variation.