Decision Tree Selection via Train-Test Consistency
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Solution Overview
Problem
Existing methods for selecting decision trees in data mining often result in overfitting, failing to ensure consistency between training and test data, particularly in terminal nodes, which can lead to unsatisfactory performance in specific nodes and discrepancies in train/test results.
Innovation Solution
A computer-implemented method that focuses on TRAIN/TEST CONSISTENCY CHECKS, using two comparisons: the AGREEMENT test to ensure similar train and test results in terminal nodes and the RANK MATCH test to verify consistent node rankings, selecting the largest tree that passes both tests as the optimal tree.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing methods for selecting decision trees are used, then the tree complexity increases, but the consistency between training and test data deteriorates
Solution Approach 1:
The patent applies preliminary action by performing consistency checks between training and test data before final tree selection. Multiple candidate trees are evaluated against both datasets in advance, and only trees demonstrating consistent performance across both datasets are selected, preventing overfitting before it occurs
Solution Approach 2:
The patent implements feedback mechanisms by using test data to evaluate and validate the performance of candidate trees. The test results feed back into the selection process, allowing iterative refinement of tree choices based on actual performance consistency between training and test scenarios
2Measurement precision
If larger trees are selected to improve classification accuracy, then the performance on training data improves, but the performance on test data deteriorates
Solution Approach 1:
The patent changes the selection parameter from pure training accuracy to a composite metric that weights both training and test performance. By adjusting this selection criterion, the method identifies trees that achieve good classification accuracy while maintaining reliability on unseen test data
Solution Approach 2:
The patent performs preliminary evaluation of candidate trees on both training and test data before final selection. This advance assessment allows identification of trees that will generalize well to test data while maintaining training accuracy, preventing the overfitting that occurs when only training performance is optimized
3Productivity
If traditional tree selection methods are used, then the overall tree performance is optimized, but the node-specific consistency between train and test data deteriorates
Solution Approach 1:
The patent segments the evaluation process to assess individual terminal nodes separately. Each node's training and test consistency is evaluated independently, allowing identification of specific nodes where overfitting occurs while preserving the overall tree performance
Solution Approach 2:
The patent applies local quality by requiring consistency not just in overall tree performance but in each individual terminal node. This ensures that every node maintains reliable train/test consistency, addressing local overfitting issues while preserving global tree effectiveness
Data Source
AI summary
The present invention provides a method and system for automatically identifying and selecting preferred classification and regression trees. The invention is used to identify a specific decision tree or group of trees that are consistent across train and test samples in node-specific details that are often important to decision makers. Specifically, for a tree to be identified as preferred by this system, the train and test samples must both agree on key measures for every terminal node of the tree. In addition to this node-by-node criterion, an additional tree selection method may be imposed. Accordingly, the train and test samples rank order the nodes on a relevant measure in the same way. Both consistency criteria may be applied in a fuzzy manner in which agreement must be close but need not be exact.