Extracting Decision Rules from Tree Ensembles for Model Interpretability
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Solution Overview
Problem
Tree ensembles, while improving model accuracy by reducing prediction variance, become non-transparent and lose interpretability when combined, making it difficult for users to understand important relationships supported by the data.
Innovation Solution
A method is provided to detect interesting decision rules in tree ensembles by assigning data records to leaf nodes, determining predicted values, and calculating novel metrics that combine interestingness and conformance with ensemble predictions, ranking decision rules based on an interestingness index, and reporting a subset of rules that are consistent with ensemble predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tree ensembles are used to improve model accuracy, then prediction accuracy is improved, but interpretability and transparency are lost
Solution Approach 1:
The patent extracts individual decision rules from the tree ensemble by identifying leaf nodes and their corresponding paths from root nodes. Each leaf node represents a specific decision rule that can be independently analyzed and reported, separating the interpretability function from the ensemble prediction function.
Solution Approach 2:
The patent introduces an intermediary evaluation process that assigns data records to leaf nodes and calculates interestingness metrics. This intermediary layer bridges the gap between the complex ensemble model and user-interpretable decision rules by providing a systematic method to evaluate and rank extracted rules based on their conformance with ensemble predictions.
2Reliability
If multiple decision rules are combined into an ensemble, then model accuracy is improved, but the complexity of interpreting individual rules increases
Solution Approach 1:
The patent segments the ensemble into individual decision rules by traversing each tree and identifying discrete leaf nodes. Each leaf node is treated as an independent unit with its own decision rule, allowing users to analyze individual rules without being overwhelmed by the entire ensemble complexity.
Solution Approach 2:
The patent applies partial action by selecting and reporting only the most interesting decision rules based on calculated metrics, rather than presenting all possible rules. This filters the excessive information to provide a manageable subset that maintains interpretability while preserving accuracy.
3Loss of information
If decision rules are extracted from tree ensembles, then interpretability is improved, but the accuracy of individual rules may deviate from ensemble predictions
Solution Approach 1:
The patent implements feedback by calculating interestingness metrics that evaluate the conformance of extracted decision rules with ensemble predictions. The metrics use data from the evaluation set to assess whether individual rules align with the overall ensemble performance, allowing iterative refinement of rule selection.
Solution Approach 2:
The patent changes parameters by introducing novel interestingness metrics that combine multiple factors: conformance with ensemble predictions, prediction accuracy, and statistical significance. These parameter transformations allow for systematic evaluation and ranking of decision rules based on their overall quality and reliability.
Data Source
AI summary
Mechanisms are provided for detecting interesting decision rules from a set of decision rules in a tree ensemble. Each tree in the tree ensemble is traversed in order to assign each individual data record from a set of data records to an identified leaf node in each tree. Predicted values are determined for the tree ensemble based on predictions provided by each leaf node to which each individual data record is assigned. Interesting sub-indices for decision rules from the set of decision rules are determined and, for each decision rule corresponding to the leaf nodes in the tree ensemble, the sub-indices are combined into interestingness index It. The decision rules are ranked corresponding to the leaf nodes in the tree ensemble according to the associated value of the interestingness index It and a subset of the decision rules corresponding to the leaf nodes in the tree ensemble are reported.