Tree Ensemble Prediction Interval Transformation
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Solution Overview
Problem
Traditional tree ensemble prediction methods are inefficient, requiring traversal of decision intervals in each tree model, which is time-consuming and problematic for time-critical applications like credit card fraud detection.
Innovation Solution
Preprocessing the tree ensemble by determining transformed intervals for each variable based on boundary points of decision intervals, allowing for quick determination of prediction results by matching input values with these intervals and traversing node hierarchies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional tree ensemble prediction methods are used, then prediction accuracy is maintained, but processing time is excessive and efficiency is low
Solution Approach 1:
The patent applies preliminary action by pre-processing the tree ensemble models before deployment. Specifically, it transforms the original decision intervals into optimized intervals and pre-determines the traversal paths for all possible input ranges. This preprocessing step creates a optimized prediction structure that eliminates the need for real-time interval traversal during actual predictions, thereby significantly reducing processing time while maintaining prediction accuracy.
Solution Approach 2:
The patent segments the continuous input space into discrete transformed intervals based on the boundary points of decision intervals from multiple tree models. By dividing the prediction process into distinct interval segments and pre-computing the traversal paths for each segment, the system avoids redundant comparisons during real-time prediction, thus improving prediction efficiency without sacrificing accuracy.
2Measurement precision
If decision intervals are traversed in each tree model, then accurate prediction results are obtained, but the complexity of the prediction process increases
Solution Approach 1:
The patent merges the decision intervals from multiple tree models into a unified set of transformed intervals. By combining and consolidating the interval boundaries from all tree models into a single optimized interval structure, the system reduces the overall complexity of the prediction process while ensuring that all necessary decision boundaries are preserved for accurate predictions.
3Measurement precision
If multiple tree models are used in the ensemble, then prediction accuracy is improved, but processing time increases linearly
Solution Approach 1:
The patent performs preliminary action by pre-computing the optimized traversal paths for all tree models in the ensemble during the offline phase. It transforms the intervals from each tree model and determines the sequence of node visits in advance. During real-time prediction, the system simply follows these pre-determined paths without performing actual interval traversals, thereby maintaining the accuracy benefits of multiple tree models while eliminating the linear time cost of traversing each model sequentially.
Data Source
AI summary
Embodiments of the present disclosure relate to methods, systems, and computer program products for optimized prediction of a tree ensemble. According to a method, an input request is received, which indicates a plurality of input values for a plurality of variables associated with a tree ensemble. A plurality of target transformed intervals, into which the plurality of input values fall respectively, are determined by matching the plurality of input values with a plurality of sets of transformed intervals for the plurality of variables respectively. Respective prediction results for a plurality of tree models of the tree ensemble are determined based on the plurality of target transformed intervals and respective node hierarchies of the plurality of tree models. A tree ensemble prediction result is determined for the input request based on the determined prediction results of the plurality of tree models.