Detecting Field Interactions via Random Tree Stumps
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Solution Overview
Problem
Existing predictive modeling solutions face difficulties in identifying field interactions, especially in higher dimensions, due to the massive number of potential combinations, and require manual specification or heuristic approaches that are inefficient.
Innovation Solution
The method generates bootstrap samples, creates decision tree stumps, encodes leaf nodes, computes quality measures, and ranks new features to automatically identify field interactions, allowing for the training of predictive models without dimensionality constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If manual specification of field interactions is used in linear regression models, then model interpretability is maintained, but the ability to detect interactions in higher dimensions is severely limited
Solution Approach 1:
The system automatically generates and evaluates potential field interactions without requiring manual specification. The algorithm self-services by systematically creating interaction terms, computing their statistics, and selecting significant interactions based on predefined criteria, eliminating the need for manual intervention while maintaining interpretability.
Solution Approach 2:
The interaction detection process is segmented into distinct stages: generating interaction terms, computing statistics for each interaction, evaluating significance based on criteria, and selecting final interactions. This segmentation allows the system to handle higher dimensions by breaking down the complex task into manageable steps.
2Productivity
If heuristic research approach is used to detect bivariate interactions, then computation time is reduced, but the detection capability in higher dimensions remains insufficient
Solution Approach 1:
The system changes parameters such as significance thresholds, interaction term generation methods, and evaluation criteria to optimize the balance between computation efficiency and detection completeness. By adjusting these parameters, the system can adapt to different dimensionality requirements while maintaining reasonable computation time.
3Measurement precision
If comprehensive interaction detection in higher dimensions is performed, then predictive model accuracy is improved, but computation time increases significantly
Solution Approach 1:
The system performs partial interaction detection by focusing on the most significant interactions rather than exhaustively analyzing all possible combinations. It uses statistical criteria to identify and evaluate only those interactions that meet significance thresholds, avoiding unnecessary computation on insignificant terms while still capturing the most important interactions for model accuracy.
Solution Approach 2:
The system uses feedback mechanisms where the results of initial interaction detection inform subsequent analysis. Significant interactions identified in early stages are prioritized for deeper analysis, while non-significant interactions are discarded. This feedback loop allows the system to adapt computation resources to the actual data characteristics, improving efficiency.
Data Source
AI summary
An approach is provided in which a method, system, and program product generate a set of bootstrap samples from a set of data records that each includes multiple fields. The method, system, and program product create a set of decision tree stumps from the set of bootstrap samples. Each one of the set of decision tree stumps includes multiple leaf nodes corresponding to one or more of the multiple fields. The method, system, and program product generate a set of new features from the set of decision tree stumps, wherein each one of the set of new features indicates at least one field interaction between two or more of the multiple fields. The method, system, and program product train a predictive model based on the set of new features.