Decision Tree Leaf Assignment Using Gray Code
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Solution Overview
Problem
Current decision tree learning methods face challenges in efficiently generating optimal decision trees from datasets, particularly in determining the best leaf assignments and decision metrics for splitting data, which affects the accuracy and complexity of the resulting models.
Innovation Solution
The method involves receiving input and target variables from a dataset, determining levels and leaf assignments using a Gray code sequence, and computing decision metrics to select the best leaf assignments, ensuring that the number of leaves is optimized and the decision metric is effectively used for data splitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional decision tree learning methods are used to determine leaf assignments and decision metrics, then the model can be generated with standard complexity, but the accuracy and optimality of the decision tree are compromised
Solution Approach 1:
The patent segments the leaf assignment determination process into discrete levels and uses Gray code to systematically enumerate possible leaf assignments at each level. This segmentation transforms the complex optimization problem into manageable discrete steps, allowing thorough evaluation of accuracy while controlling computational complexity through structured decomposition.
Solution Approach 2:
The patent performs preliminary actions by pre-defining the number of levels and maximum number of leaves before computing decision metrics. By establishing these parameters in advance and using Gray code to pre-structure the search space, the method prepares the computational framework beforehand, enabling more accurate decision tree generation without proportional increases in computational complexity during execution.
2Reliability
If the number of leaves is increased to improve model accuracy, then the predictive performance improves, but the computational complexity and model complexity increase
Solution Approach 1:
The patent applies dynamics by allowing the number of leaves to vary up to a maximum threshold while using Gray code to dynamically adjust leaf assignments across different levels. This dynamic approach enables the model to achieve higher predictive performance when beneficial, while maintaining control over complexity through the structured progression defined by Gray code sequences and level-based constraints.
Solution Approach 2:
The patent changes parameters by systematically varying leaf assignment configurations across different levels using Gray code. This parameter change strategy allows the model to explore different complexity levels and select the optimal number of leaves for predictive performance, balancing model accuracy with controlled complexity through structured parameter exploration.
3Measurement precision
If exhaustive search of all possible leaf assignments is performed to ensure optimality, then the best decision tree can be found, but the computational time and resources required increase significantly
Solution Approach 1:
The patent implements periodic action through the systematic use of Gray code sequences to enumerate leaf assignments in a structured, periodic manner. This periodic enumeration ensures that all possible assignments are evaluated for optimality while following a regular computational pattern that reduces redundant calculations and optimizes resource utilization compared to random or unstructured exhaustive search methods.
Solution Approach 2:
The patent maintains continuity of useful action by using Gray code to ensure that each successive leaf assignment evaluation builds upon previous computations. The Gray code structure allows incremental updates and continuous progression through the search space, maintaining computational momentum and avoiding repeated calculations, thus finding optimal decision trees more efficiently than discontinuous exhaustive search approaches.
Data Source
AI summary
A method of generating a decision tree is provided. A leaf assignment for each proposed split in generating the decision tree is incremented using a Gray code.