ML Model Optimization via Ensemble Tree Residual Analysis
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Solution Overview
Problem
Machine learning models experience performance decay due to data drift and concept drift, leading to inaccurate predictions, and replacing them is resource-intensive, risky, and difficult to implement, especially in black box environments where transparency and interpretability are lacking.
Innovation Solution
A computer-implemented method and system that uses ensemble decision trees to identify sub-optimal pockets in existing machine learning models by analyzing prediction errors and applying these errors as a target variable to generate interpretable rules for optimizing the model, thereby improving performance without replacing the entire model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the existing machine learning model is replaced with a new model built from scratch, then prediction accuracy may be improved, but computational resources required and implementation difficulty increase significantly
Solution Approach 1:
The patent segments the model improvement process into two distinct phases: (1) training a new model on historical data to identify performance patterns, and (2) applying learned adjustments to the existing deployed model. This segmentation allows the new model to be trained offline without disrupting production systems, reducing implementation complexity while maintaining accuracy improvement benefits.
Solution Approach 2:
The patent performs preliminary actions by training the new model on historical data before deploying it to production. This preliminary training phase allows the system to learn from past performance patterns and prepare optimization strategies in advance, so that when the model is applied to the existing system, the adjustments are already optimized and ready to improve accuracy without requiring complex real-time retraining.
2Measurement precision
If the existing machine learning model is replaced with a new model built from scratch, then prediction accuracy may be improved, but computational resources required increase
Solution Approach 1:
The patent applies partial action by training the new model only on historical data relevant to performance patterns, rather than retraining on all possible data. This selective training approach reduces computational resources required while still capturing the essential patterns needed to improve prediction accuracy on the existing model.
Solution Approach 2:
The patent creates a copy of the training process using historical data to generate a new model, then applies the learned patterns to the existing deployed model. This copying approach allows the system to benefit from comprehensive training without requiring the production system to undergo resource-intensive retraining, as the optimization knowledge is transferred through the copied training results.
3Measurement precision
If the existing machine learning model is replaced with a new model built from scratch, then prediction accuracy may be improved, but system disruption and risk increase
Solution Approach 1:
The patent implements beforehand cushioning by training the new model on historical data and validating its performance before applying it to the existing production model. This preparatory validation phase acts as a cushion, ensuring that the new model's adjustments are proven effective and stable before deployment, thereby reducing system disruption and risk while maintaining reliability.
4Measurement precision
If the existing machine learning model is updated to improve performance, then prediction accuracy improves, but interpretability and transparency are reduced
Solution Approach 1:
The patent introduces an intermediary layer that captures performance patterns from historical data and translates them into adjustments for the existing model. This intermediary representation maintains the interpretability of the original model structure while incorporating accuracy improvements, as the adjustments are derived from observable performance patterns rather than replacing the entire model architecture.
Data Source
AI summary
Computational systems and methods are provided to automatically assess residual characteristics of an existing machine learning model to identify and determine suboptimal pockets and augmentation strategies. A computing system, device and method for optimizing a machine learning model for performing predictions is provided. The computing device performs sub-optimal pocket identification on an existing machine learning algorithm by residual analysis to calculate an error. The computing device utilizes the residual as a target for an ensemble tree model and automatically generates a set of interpretable rules from the tree based ensemble model that contribute to the suboptimal pockets. The rules indicating relationships between features and interactions as well as values for the sub-optimal pockets. The computing device determines optimizations for improving the machine learning model based on the interpretable computer-implemented rules.