Dataset Drift Split Generation for ML Model Evaluation
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Solution Overview
Problem
Current machine learning experimentation methods provide limited insights due to random train-test splits, which do not effectively evaluate algorithm performance on corner cases or drift scenarios, leading to inadequate testing of machine learning models.
Innovation Solution
A system and method for systematically generating dataset drift splits, executing experimental jobs, processing and clustering results, and providing recommendations through an interactive dashboard to evaluate machine learning model performance across various data set drift scenarios, including stress testing for domain shift.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If random train-test splits are used, then the experimentation process is simple and quick, but the evaluation insights are limited and do not cover corner cases or drift scenarios
Solution Approach 1:
The patent segments the test dataset into multiple stratified subsets based on feature distributions, creating distinct drift scenarios (e.g., feature drift, target drift, concept drift). This segmentation allows comprehensive evaluation across different corner cases while maintaining systematic control over each scenario's characteristics.
Solution Approach 2:
The patent performs preliminary actions by pre-defining multiple drift scenario configurations and preparing stratified test splits before actual model evaluation. This advance preparation enables systematic coverage of various drift conditions without ad-hoc modifications during experimentation.
2Loss of information
If systematic data set drift splits are generated and executed, then deeper insights into model performance are obtained, but the experimentation scale and complexity increase
Solution Approach 1:
The patent creates a universal framework that handles multiple types of data drift scenarios (feature drift, target drift, concept drift, covariate shift) using a single stratified splitting mechanism. This multi-functional approach allows the same system to evaluate models across diverse drift conditions without requiring separate specialized processes for each scenario type.
Solution Approach 2:
The patent systematically varies key parameters such as drift magnitude, drift type, and feature selection to create multiple test scenarios. By controlling and adjusting these parameters, the system comprehensively evaluates model robustness across different drift conditions while maintaining experimental consistency.
3Reliability
If cross-validation with random splits is used, then the process is straightforward, but it does not provide insights into performance on corner cases or drift scenarios
Solution Approach 1:
The patent segments the validation process by creating stratified test folds that specifically target different drift scenarios. Each fold is constructed to represent a particular corner case or drift condition, ensuring that model reliability is assessed under diverse and realistic conditions rather than uniform random sampling.
Solution Approach 2:
The patent introduces an intermediary stratification layer between the raw data and the cross-validation process. This intermediary step ensures that each fold maintains representative feature distributions and drift characteristics, acting as a mediator that preserves corner case information throughout the validation process.
Data Source
AI summary
A computer implemented method of performing large-scale machine learning experiments includes expanding on one or more input datasets by systematically generating several data set drift splits. A set of experimental jobs corresponding to the generated data set drift splits are executed to generate experimental results. The experimental results are processed, consolidated, and clustered according to the generated data set drift splits.