Automated ML Model Selection via Subset Evaluation
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Solution Overview
Problem
Evaluating and selecting the best-performing machine learning model from multiple models trained for the same task is time and resource intensive, especially when considering the entire training dataset, which can lead to inefficiencies and longer training times.
Innovation Solution
The method involves obtaining a subset of training data, generating predicted values using multiple machine learning models, computing differential values for performance metrics like precision and recall using a linear regression model, and selecting the model based on confidence intervals and p-values, thereby reducing computational resources and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire training dataset is used to evaluate and select machine learning models, then the reliability of model selection is improved, but the training time and computational resources increase
Solution Approach 1:
The patent applies partial action by evaluating models on a subset of the training data rather than the entire dataset. The system identifies a proper subset of training data items and evaluates model performance metrics on this subset, which reduces computational resources and training time while still providing reliable model selection through statistical confidence intervals and p-values.
2Measurement precision
If the entire training dataset is used to evaluate and select machine learning models, then the measurement precision of model performance is improved, but the computational resources increase
Solution Approach 1:
The patent uses partial action by computing model performance metrics on a subset of training data rather than the full dataset. This approach measures precision, recall, and other performance indicators on the subset, reducing computational resource usage while maintaining statistically valid measurements through confidence intervals and hypothesis testing.
3Measurement precision
If multiple machine learning models are evaluated on the entire training dataset, then the accuracy of model comparison is improved, but the productivity decreases
Solution Approach 1:
The patent applies partial action by evaluating multiple models on a subset of the training dataset rather than the entire dataset. This significantly improves productivity and model selection efficiency while maintaining accurate model comparison through statistical methods including confidence intervals and p-value calculations that validate the reliability of comparisons made on the subset.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for evaluating and comparing multiple trained machine learning models. Methods can include generating, using a first and a second machine learning model, a respective predicted value for the target attribute. The methods compute a differential value for a model performance metric indicating a difference in the respective model performance attribute values and a corresponding confidence interval that indicates a probability that the differential value accurately reflects the difference in the respective model performance attribute values using a linear regression model and the respective predicted values. The methods then select based on the computed confidence interval a machine learning model. The methods obtain a set of actual data items encountered in a production environment, and use the selected machine learning model to generate a corresponding set of predicted values for the target attribute.