Progressive-Depth Gradient Boosted Trees for Interpretable GLM Structures
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Solution Overview
Problem
Existing generalized linear models (GLMs) and gradient boosting techniques face challenges in providing transparent and interpretable models for insurance pricing, leading to inefficiencies in regulatory compliance and predictive accuracy due to the inability to identify and remove control variable effects, which are often double-counted.
Innovation Solution
The method involves partitioning decision trees of a gradient boosted tree model into a plurality of indicator variables, allowing for the generation of a generalized linear model structure definition that enhances interpretability and accuracy by explicitly identifying and separating main and interaction effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gradient boosting is used to maximize predictive accuracy, then predictive accuracy is improved, but clarity of interpretation deteriorates
Solution Approach 1:
The patent segments the gradient boosted decision tree model into multiple components: decision trees for predictive accuracy and a separate generalized linear model for interpretability. The GLM is trained on the same data but with a simplified structure that explicitly models main effects and interaction effects, allowing clear identification of variable contributions without the complexity of the full boosting ensemble.
Solution Approach 2:
The patent introduces a generalized linear model as an intermediary representation that mediates between the complex gradient boosted tree model and the need for interpretation. The GLM serves as a transparent surrogate that captures the essential relationships in the data while maintaining mathematical tractability and interpretability, allowing regulators and stakeholders to understand the pricing logic without needing to interpret the complex tree ensemble.
2Ease of operation
If manual specification of variables is used in GLMs, then clarity of interpretation is improved, but labor intensity increases
Solution Approach 1:
The patent implements self-service by automatically extracting variable specifications and interaction terms from the gradient boosted tree model structure. The system automatically identifies which variables should be included in the GLM based on the tree's splitting structure, eliminating the need for manual variable selection by domain experts while maintaining interpretability.
Solution Approach 2:
The patent changes the approach from manual variable specification to automated parameter extraction. The GLM's coefficient parameters are automatically estimated from the gradient boosting model's leaf node predictions and tree structure, transforming a labor-intensive manual process into an automated computational process that maintains interpretability.
3Device complexity
If control variables are not removed from GLM relativities, then model simplicity is maintained, but predictive accuracy deteriorates due to double counting
Solution Approach 1:
The patent extracts control variables from the GLM relativity calculations. By separating control variables (such as geographic location or policy type) from the relativity factors, the model avoids double counting their effects. The control variables are handled separately in the base rate or through fixed effects, while the relativity factors focus only on risk-related variables, improving the accuracy of the pricing model.
Data Source
AI summary
An apparatus for generating a generalized linear model structure definition by generating a gradient boosted tree model and separating each decision tree into a plurality of indicator variables upon which a dependent variable of the generalized linear model depends.