Automated Interaction Detection in Predictive Models
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Solution Overview
Problem
Conventional predictive models are limited by the manual examination of interactions between predictor variables, which is infeasible in models with a large number of variables, leading to overlooked potential interactions and suboptimal performance.
Innovation Solution
Automated detection and evaluation of all possible pairs of predictor variables using statistical tests for complete spatial randomness, followed by encoding interactions into Boolean functions and additional predictive features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual examination of predictor variable pairs is performed, then modeler can identify intuitive interactions, but all possible pairs cannot be evaluated when the number of variables is large
Solution Approach 1:
The patent replaces manual mechanical examination of variable pairs with automated computer-based statistical analysis. The system automatically generates all possible pairs of predictor variables and applies spatial randomness tests to each pair, eliminating the limitation of manual review while maintaining detection accuracy.
Solution Approach 2:
The patent transforms the examination process by changing from manual inspection to automated statistical testing. It uses spatial randomness parameters and automated algorithms to evaluate interactions, enabling comprehensive analysis of all variable pairs regardless of dataset size.
2Reliability
If all possible pairs of predictor variables are evaluated, then no interactions are overlooked, but the complexity of the analysis increases significantly
Solution Approach 1:
The patent extracts only the essential information needed for interaction detection by using spatial randomness tests that evaluate the joint distribution of variable pairs. This approach identifies interactions without requiring complex manual analysis of all possible relationships, reducing analytical complexity while maintaining completeness.
Solution Approach 2:
The system performs self-service by automatically generating all variable pairs, conducting spatial randomness tests, and identifying interactions without human intervention. The automated process handles the complexity internally, allowing comprehensive evaluation without proportionally increasing operational complexity.
3Productivity
If automated evaluation of all predictor variable pairs is performed, then model development time is reduced, but computational resources are consumed
Solution Approach 1:
The patent applies partial action by using spatial randomness tests that provide sufficient information for interaction detection without requiring exhaustive analysis of all possible interaction mechanisms. This approach achieves adequate detection accuracy with reduced computational effort compared to comprehensive manual analysis of all variable relationships.
Data Source
AI summary
Techniques for detecting interactions between predictor variables in a statistical model are provided. The techniques include identifying predictor variables for a dependent variable; and for each pair of predictor variables: obtaining a dataset including (i) first predictor variable values, (ii) second predictor variable values, and (iii) dependent variable values associated with each pair of a first predictor variable value and a second predictor variable value; generating a three-dimensional graph based on the dataset, wherein each point of the three-dimensional graph includes a first coordinate value associated with a first predictor variable, a second coordinate value associated with a second predictor variable, and a third coordinate value associated with a dependent variable outcome; and analyzing the three-dimensional graph to determine a measure of spatial randomness associated with the three-dimensional graph. The techniques further include identifying pairs of predictor variables having interactions based on their respective measures of spatial randomness.