Predictive Accuracy Gain Estimation Without Retraining
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Solution Overview
Problem
Traditional methods for evaluating the predictive accuracy gain of new features in machine learning systems are computationally, logistically, and monetarily costly, especially in large-scale scenarios, hindering rapid experimentation and feature design due to the need for complete retraining of algorithms.
Innovation Solution
Estimating predictive accuracy gain by using existing predictor outputs and labels, without retraining, by computing loss gradients and training an incremental predictor to maximize correlation with potential feature values, allowing for efficient evaluation of feature relevance without augmenting the feature set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional complete retraining approach is used to evaluate predictive accuracy gain from a potential feature, then measurement precision of feature relevance is improved, but productivity of feature design and experimentation deteriorates due to computational cost and time consumption
Solution Approach 1:
The patent performs preliminary computation of loss gradients and feature correlations before final feature selection. By pre-computing these statistical measures on the existing trained model, the system avoids the need for complete retraining when evaluating potential features, thus maintaining measurement precision while improving experimentation speed
Solution Approach 2:
The patent uses a copy or approximation approach by computing correlations between potential features and loss gradients derived from the existing model outputs. This copying of gradient information from the trained model allows accurate feature evaluation without actually retraining the model with the new feature
2Reliability
If traditional complete retraining approach is used to evaluate potential features, then reliability of feature evaluation is improved, but loss of time increases due to computational requirements
Solution Approach 1:
The system performs preliminary computation of loss gradients and statistical correlations before final feature selection. This preliminary action maintains evaluation reliability by using rigorous statistical measures while avoiding the time-consuming complete retraining process
Solution Approach 2:
The patent changes the evaluation parameter from complete model retraining to computing correlations between potential features and loss gradients. This parameter change maintains reliability through statistical rigor while dramatically reducing the time required for feature evaluation
3Measurement precision
If complete retraining with augmented features is performed, then accuracy improvement measurement is improved, but device complexity and operational difficulty increase due to infrastructure pipeline requirements
Solution Approach 1:
The patent extracts the essential evaluation information (loss gradients and feature correlations) from the trained model without requiring the entire training pipeline. By taking out only the necessary computational elements, the system simplifies the evaluation process while maintaining measurement precision
Solution Approach 2:
The loss gradient computation acts as an intermediary that bridges the gap between the existing model and potential features. This intermediary allows accurate feature evaluation by computing correlations with the gradient, avoiding the need to integrate the full training pipeline complexity
Data Source
AI summary
Various technologies described herein pertain to estimating predictive accuracy gain of a potential feature added to a set of features, wherein an existing predictor is trained on the set of features. Outputs of the existing predictor for instances in a dataset can be retrieved from a data store. Moreover, a predictive accuracy gain estimate of a potential feature added to the set of features can be measured as a function of the outputs of the existing predictor for the instances in the dataset. The predictive accuracy gain estimate can be measured without training an updated predictor on the set of features augmented by the potential feature.