Decision Tree Parameter Optimization for Smaller Accurate Models
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Solution Overview
Problem
Existing methods for learning decision trees, such as CART-type algorithms, are suboptimal in terms of prediction accuracy, interpretability, model size, speed of learning, and inference speed, due to their greedy growth and non-differentiable nature, which leads to suboptimal and larger trees that overfit training data.
Innovation Solution
The Tree Alternating Optimization (TAO) algorithm optimizes a predefined tree structure by iteratively updating node parameters to minimize a defined objective function, incorporating various loss functions and regularization terms, allowing for better decision trees with reduced size and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If greedy growing algorithm is used to learn decision trees, then the learning speed is fast, but the prediction accuracy is suboptimal and the tree size is larger
Solution Approach 1:
The patent segments the decision tree learning process into two distinct phases: (1) structure learning using greedy growing to quickly establish the tree topology, and (2) parameter optimization using alternating optimization to refine node parameters. This segmentation allows each phase to specialize - greedy growing provides fast structure discovery while alternating optimization delivers precise parameter tuning, resolving the contradiction between learning speed and prediction accuracy.
Solution Approach 2:
The alternating optimization procedure continuously refines parameters by alternating between optimizing decision node parameters and leaf node parameters until convergence. This continuous iterative improvement maintains the useful action of parameter refinement without interrupting the overall learning process, thereby improving prediction accuracy while building upon the initially fast-constructed tree structure.
2Productivity
If greedy growing algorithm is used to learn decision trees, then the learning speed is fast, but the tree size becomes larger
Solution Approach 1:
By separating structure learning from parameter optimization, the patent enables greedy growing to focus solely on creating an efficient tree structure quickly, while the subsequent parameter optimization phase refines the model without adding structural complexity. This segmentation prevents the tree from growing unnecessarily large while maintaining fast initial learning.
Solution Approach 2:
The alternating optimization procedure changes parameters iteratively - first optimizing decision node parameters then leaf node parameters, repeating until convergence. This parameter refinement improves model compactness and performance without increasing tree size, as it works within the fixed structure established by greedy growing rather than expanding the structure further.
3Ease of operation
If traditional decision tree methods are used, then the model is simple to interpret, but the prediction accuracy is suboptimal
Solution Approach 1:
The patent maintains interpretability by keeping the decision tree structure simple and hierarchical, while applying complex optimization only to the parameters within this interpretable framework. The segmented approach allows the tree structure to remain visually and conceptually simple for interpretation, while the parameter optimization silently improves accuracy in the background without affecting the tree's interpretability.
Solution Approach 2:
By optimizing parameters within the existing tree structure rather than fundamentally changing the tree architecture, the patent improves prediction accuracy while preserving the simple, interpretable decision tree format. The parameter changes occur within the same structural framework, maintaining ease of interpretation while achieving better performance.
4Manufacturing precision
If larger decision trees are produced to improve accuracy, then the prediction accuracy improves, but the inference speed decreases
Solution Approach 1:
The alternating optimization procedure refines parameters within the existing tree structure to improve accuracy, rather than expanding the tree size. By optimizing the parameters of decision nodes and leaf nodes iteratively, the patent achieves better prediction accuracy while maintaining the original tree's compact structure and fast inference capabilities.
Data Source
AI summary
A computer-implemented Tree Alternating Optimization (TAO) algorithm for learning decision trees to find an approximate minimizer of an objective function over the parameters of the tree. Generally, the method comprises inputting an initial decision tree and a training set of instances, processing the initial decision tree by partitioning nodes into sets of non-descendant nodes, processing the nodes in each set by updating the nodes' parameters at each iteration so that the objective function decreases monotonically, and pruning the tree, which produces a final tree of a size no larger than that of the initial tree. TAO applies to many different types of loss functions, regularization terms and constraints, and types of models at both the decision nodes and the leaves, and makes it possible to learn better decision trees than with traditional algorithms, and to learn trees for problems where traditional algorithms do not apply.