Single-Index Model Tree for Interpretable Machine Learning
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Solution Overview
Problem
Highly accurate machine learning models are often not interpretable, making it difficult for users to understand the reasoning behind their outputs, which is a growing concern as interpretability becomes a critical factor in model selection.
Innovation Solution
The development of a Single-Index Model (SIM) tree that uses fast SIM estimation via Stein's lemma to generate split points and variables, resulting in a highly accurate and interpretable model by fitting SIMs to each leaf node and providing a graphical representation of the tree structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If highly accurate machine learning models are used, then prediction accuracy is improved, but interpretability deteriorates
Solution Approach 1:
The patent segments the complex machine learning model into a tree structure with multiple nodes, where each node represents a simpler decision unit. This segmentation allows the model to maintain high accuracy through multiple splits while improving interpretability by breaking down the decision-making process into visually representable, understandable segments that can be explained to users.
2Measurement precision
If complex models are used to improve accuracy, then prediction performance is improved, but ease of operation deteriorates
Solution Approach 1:
The patent introduces an intermediary visual representation layer between the complex model and the user. The tree structure with nodes and splits acts as an intermediary that translates complex model decisions into a visual format that users can understand, maintaining prediction performance while improving ease of operation through intuitive graphical display.
3Measurement precision
If black-box models are used, then prediction accuracy is improved, but loss of information deteriorates
Solution Approach 1:
The patent enables the model to self-explain its decision-making process through the tree structure. Each node and split provides self-service information about the reasoning process, allowing users to understand how predictions are made without requiring external interpretation tools, thus reducing information loss while maintaining accuracy.
Data Source
AI summary
Systems, apparatuses, methods, and computer program products are disclosed for generating a single-index model (SIM) tree. An example method includes receiving a data set and a maximum tree depth. The example method further includes screening a set of variables from the data set to form split variables. The method may include, while maximum tree depth has not been reached, (i) generating a fast SIM estimation for nodes of a tree level, (ii) for each node, selecting a split point and split variable based on the fast SIM estimation, (iii) based on the selected split points and split variables, generating nodes for a next tree level, each including a subset of data, and (iv) repeating steps (i), (ii), and (iii). The method may include fitting a SIM for each leaf node at maximum tree depth based on a subset of the data set represented by the leaf node.