ML Test Data Distribution Validation
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Solution Overview
Problem
Machine learning model development is hindered by the need for expertise in statistics and AI, and the unpredictability of computational resources required, leading to inefficiencies and increased costs due to differences in statistical distributions between training and test data sets.
Innovation Solution
A network-accessible machine learning service that analyzes and validates the distribution of target variables to ensure similarity between training and test data sets, using algorithms like Kullback-Leibler divergence and Kolmogorov-Smirnov statistics to determine acceptability and recommend split algorithms, thereby optimizing resource usage and model quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a data set is split into training and test subsets for machine learning model evaluation, then the model quality can be assessed, but the target variable distribution may differ between subsets causing inaccurate evaluation and increased resource consumption
Solution Approach 1:
The patent applies preliminary action by performing distribution comparison analysis on the test data set before the machine learning model training and evaluation process. Statistical tests (Kolmogorov-Smirnov, Chi-squared, Anderson-Darling) are executed on the target variable distribution of the test subset to determine if it matches the training subset distribution. This preliminary check prevents wasteful computation of full model training when the test data distribution is incompatible, thereby resolving the contradiction between accurate evaluation and resource consumption.
2Measurement precision
If expertise in statistics and artificial intelligence is required for machine learning model development, then model quality can be improved, but the barrier to entry becomes too high for business analysts and subject matter experts
Solution Approach 1:
The patent implements self-service by providing automated distribution validation that performs statistical analysis without requiring user expertise. The system automatically executes multiple statistical tests (Kolmogorov-Smirnov, Chi-squared, Anderson-Darling) on the data sets, interprets the results, and generates pass/fail determinations. This automation allows business analysts and subject matter experts to validate their data sets for machine learning without needing to understand complex statistical concepts, thus resolving the contradiction between model quality and ease of operation.
3Measurement precision
If the target variable distribution in the test subset differs from the training subset, then the model evaluation may appear worse than it should be, but this leads to unnecessary re-training and re-testing that increases overall cost
Solution Approach 1:
The patent applies preliminary action by performing distribution comparison analysis on the test data set before the machine learning model training and evaluation process. Statistical tests (Kolmogorov-Smirnov, Chi-squared, Anderson-Darling) are executed on the target variable distribution of the test subset to determine if it matches the training subset distribution. This preliminary check prevents wasteful computation of full model training when the test data distribution is incompatible, thereby resolving the contradiction between accurate evaluation and resource consumption.
Data Source
AI summary
Respective statistical distributions of a target variable within a proposed training data set and a proposed test data set for a machine learning model are obtained. A metric indicative of the difference between the two statistical distributions is computed. The difference metric is used to determine whether the proposed test data set is acceptable to evaluate the machine learning model.