Pairwise Interaction Detection Tool for Model Explainability
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Solution Overview
Problem
Conventional machine learning models are unable to detect and incorporate interactions between predictors, leading to inconsistent and unreliable explanations of model outputs, as they fail to quantify marginal contributions of interaction effects and apply pattern constraints effectively.
Innovation Solution
A pairwise interaction detection tool that performs two-dimensional binning and risk pattern detection, comparing the divergence of two machine learning models to predict the strength of interaction effects between predictors, and generates visualizations to represent these interactions, thereby improving model explainability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning models are used, then model training is simple and fast, but the models cannot detect and incorporate interactions between predictors, leading to inconsistent and unreliable explanations
Solution Approach 1:
The patent segments the interaction detection process into distinct steps: binning input samples in multiple dimensions, determining risk patterns separately for each dimension, and then combining them to form two-dimensional risk patterns. This segmentation allows complex interaction effects to be detected and incorporated systematically, improving explanation reliability while managing model complexity through structured decomposition.
Solution Approach 2:
The patent transitions from one-dimensional predictor analysis to two-dimensional interaction analysis by binning samples across multiple dimensions simultaneously. This dimensional expansion enables the detection of interaction effects between predictors that conventional one-dimensional models cannot capture, thereby improving explanation reliability without requiring entirely new model architectures.
2Reliability
If interaction effects are incorporated into the model, then explanation reliability improves, but the computational time and complexity of model training increases
Solution Approach 1:
The patent performs preliminary binning of input samples and determination of risk patterns before final model training. By pre-processing the data into binned categories and pre-calculating risk patterns for each dimension, the system reduces computational complexity during actual model training, thereby limiting time loss while still capturing interaction effects to improve explanation reliability.
3Measurement precision
If two-dimensional binning and risk pattern detection are performed, then interaction effects are detected accurately, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the complex two-dimensional binning process into manageable steps: first binning along one dimension, then binning along the second dimension independently, and finally combining the results. This segmentation reduces processing complexity by breaking down the computationally intensive task into smaller, more manageable operations while maintaining measurement precision for interaction detection.
Solution Approach 2:
The patent transforms continuous predictor values into discrete binned categories, changing the parameter representation from continuous to discrete. This parameter transformation simplifies subsequent processing and reduces computational complexity while maintaining the precision needed to detect interaction effects, as the binned categories capture the essential variation in the data.
Data Source
AI summary
A method is provided for implanting a pairwise interaction detection tool. The method includes binning input samples in a first dimension associated with a first predictor of an outcome based at least on a sample minimum, binning the input samples in a second dimension associated with a second predictor of the outcome, determining a two-dimensional risk pattern based at least on a first one-dimensional risk pattern associated with the first predictor along the first dimension and a second one-dimensional risk pattern associated with the second predictor along the second dimension, comparing a first divergence of a first machine learning model to a second divergence of a second machine learning model, and predicting a strength of an interaction effect between the first predictor and the second predictor based on the comparison. Related methods and articles of manufacture are also disclosed.