Tree-Based ML Model Monotonicity Enforcement
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Solution Overview
Problem
Existing machine learning models struggle to efficiently compute predicted outputs and generate explanatory data while ensuring monotonicity with respect to independent variables.
Innovation Solution
The development of tree-based machine learning models that enforce monotonicity by iteratively adjusting splitting rules and representative response values, allowing the same model to predict outputs and generate explanatory data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional machine learning models are used to predict outputs, then prediction capability is achieved, but computational efficiency and resource allocation deteriorate when also generating explanatory data
Solution Approach 1:
The patent applies universality by designing a single tree-based machine learning model that performs multiple functions: both predicting outputs and generating explanatory data about variable impacts. This eliminates the need for separate models for prediction and explanation, thereby improving computational efficiency while maintaining comprehensive functionality.
Solution Approach 2:
The patent merges the prediction function and explanation generation function into a unified tree-based model. By combining these previously separate operations into one model structure, the system achieves both prediction and interpretability simultaneously, reducing overall computational overhead and resource requirements.
2Reliability
If tree-based models enforce monotonicity by iteratively adjusting splitting rules, then model accuracy and reliability improve, but training time and computational resources increase
Solution Approach 1:
The patent applies dynamics by implementing an iterative adjustment process for splitting rules that adapts during training. The model dynamically modifies its structure to enforce monotonicity constraints, allowing it to achieve reliable, monotonic predictions while managing training complexity through progressive refinement rather than rigid pre-specification.
Solution Approach 2:
The patent changes parameters (splitting rules and representative response values) iteratively during training to enforce monotonicity. By adjusting these parameters progressively, the model achieves reliable monotonic behavior without requiring excessive training time, as the parameter changes are guided by specific monotonicity constraints rather than exhaustive search.
3Adaptability or versatility
If separate models are used for prediction and explanation, then functional completeness is achieved, but system complexity and resource allocation deteriorate
Solution Approach 1:
The patent applies universality by designing a single tree-based machine learning model that performs multiple functions: both predicting outputs and generating explanatory data about variable impacts. This eliminates the need for separate models for prediction and explanation, thereby improving computational efficiency while maintaining comprehensive functionality.
Solution Approach 2:
The patent segments the model's functionality into distinct components (prediction function and explanation generation) that operate within a unified tree-based structure. This segmentation allows the model to handle different tasks separately yet efficiently, maintaining functional completeness while avoiding the overhead of entirely separate systems.
Data Source
AI summary
Certain aspects involve training tree-based machine-learning models for computing predicted responses and generating explanatory data for the models. For example, independent variables having relationships with a response variable are identified. Each independent variable corresponds to an action or observation for an entity. The response variable has outcome values associated with the entity. Splitting rules are used to generate the tree-based model, which includes decision trees for determining relationships between independent variables and a predicted response associated with the response variable. The tree-based model is iteratively adjusted to enforce monotonicity with respect to representative response values of the terminal nodes. For instance, one or more decision trees are adjusted such that one or more representative response values are modified and a monotonic relationship exists between each independent variable and the response variable. The adjusted model is used to output explanatory data indicating relationships between independent variable changes and response variable changes.