Model Configuration Portfolio Using Regret Matrix Selection
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Solution Overview
Problem
Conventional methods for selecting and training machine learning models are resource-intensive, expensive, and often result in overfitting, making it difficult to determine which model to use effectively for a given input task, especially when dealing with unknown data.
Innovation Solution
A configuration portfolio management system that generates a regret matrix to evaluate and select a compact set of model configurations based on regret metrics, considering a diverse range of training tasks with varying metafeatures, allowing for efficient selection of a model configuration using a zero-shot algorithm without requiring additional training on the input task.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of machine learning models are trained to ensure effective model selection, then model selection accuracy is improved, but computing resource consumption and cost increase significantly
Solution Approach 1:
The patent extracts only the most promising model configurations from the search space by using regret metrics to identify and eliminate inferior configurations early in the process. Instead of training all possible models, the system extracts a compact subset of candidate configurations that are most likely to perform well, thereby reducing computing resource consumption while maintaining model selection accuracy.
Solution Approach 2:
The patent performs preliminary evaluation of model configurations using regret metrics before full training. By calculating regret values based on partial training results and eliminating configurations with high regret, the system performs useful filtering action in advance, avoiding the need to train and evaluate all possible model configurations, thus reducing overall computing resource consumption.
2Adaptability or versatility
If a large number of machine learning models are trained to cover various input tasks, then model versatility is improved, but training time and cost increase significantly
Solution Approach 1:
The patent creates a compact portfolio of model configurations that are universally applicable across diverse input tasks. By selecting configurations that perform well across multiple task types using regret metrics, the system achieves versatility with a small number of models, eliminating the need to train separate models for each task type and thereby reducing total training time.
Solution Approach 2:
The patent changes the parameter selection criteria from traditional performance metrics to regret metrics, which measure the opportunity cost of not selecting the best configuration. This parameter change enables the system to identify versatile configurations more efficiently, achieving broad task coverage with fewer trained models and reduced training time.
3Reliability
If conventional model selection tools are used to determine effective models, then model effectiveness is improved, but implementation cost increases
Solution Approach 1:
The patent uses inexpensive regret metric calculations as a disposable preliminary screening tool to evaluate model configurations. Instead of relying on expensive conventional model selection tools for every configuration, the system uses cheap regret-based filtering to eliminate inferior models early, reserving expensive evaluation methods only for the final compact subset of promising candidates, thereby reducing overall implementation cost while maintaining model effectiveness.
4Adaptability or versatility
If a sizable collection of machine learning models is obtained, then model selection coverage is improved, but determining which model to use becomes computationally prohibitive
Solution Approach 1:
The patent extracts a compact subset of model configurations from the full search space by using regret metrics to identify and remove inferior configurations. This extraction process reduces the model collection size dramatically while preserving the most effective configurations, making subsequent model selection computationally feasible without sacrificing coverage of effective models.
Solution Approach 2:
The patent performs preliminary filtering of model configurations using regret calculations before the final model selection step. By eliminating configurations with high regret values in advance, the system reduces the complexity of the subsequent model selection process, making it computationally manageable while still maintaining broad coverage of effective model types.
Data Source
AI summary
This disclosure relates to implementing a configuration portfolio having a compact set of model configurations that are predicted to perform well with respect to a wide variety of input tasks. Systems described herein involve evaluating machine learning models with respect to a set of training tasks to generate a regret matrix based on accuracy of the machine learning models in connection with predicting outputs for the training tasks. The systems described herein can identify a subset of model configurations from a plurality of model configurations based on the subset of model configurations having lower associated metrics of regret with respect to the training tasks. This ensures that each model configuration within the configuration portfolio will perform reasonably well for a given input task and provides a mechanism for selecting an output model configuration using significantly fewer processing resources than conventional model selection systems.