Machine Learning Model Stability via Bayesian Optimization
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Solution Overview
Problem
Existing machine learning models face challenges in stability across multiple samples due to high Kolmogorov-Smirnov (KS) differences, leading to overfitting or underfitting, and require frequent rebuilding, while also being complex and resource-intensive.
Innovation Solution
A method that engages users in a dialogue for customizing feature selection and model development, using a parametric search process with Bayesian optimization to generate optimized parameter sets, calculate KS statistics, and reduce model complexity by selecting relevant features and clusters, thereby producing a stable model across different samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced machine algorithms are used to improve model performance, then predictive accuracy is improved, but model stability deteriorates due to overfitting or underfitting
Solution Approach 1:
The system performs parametric search by systematically varying model parameters and hyperparameters across multiple iterations. It generates optimized parameter sets using Bayesian optimization and evaluates models based on KS statistics, selecting parameter combinations that achieve both high predictive accuracy and stability across different samples.
Solution Approach 2:
The system implements feedback mechanisms by calculating KS statistics for each model iteration and using this information to guide subsequent parameter optimization. The dialogue-based interface provides feedback to users about model performance, allowing them to adjust feature selection criteria and model parameters based on observed stability and accuracy metrics.
2Measurement precision
If model complexity is increased to improve predictive performance, then model accuracy is improved, but computational cost and memory usage increase
Solution Approach 1:
The system extracts and selects only the most relevant features from the input data through automated feature selection processes. It identifies and removes redundant or less important variables, retaining only those features that contribute most to predictive performance while reducing overall model complexity and computational requirements.
Solution Approach 2:
The system performs partial feature selection by selecting a subset of the most important features rather than using all available features. This partial action approach achieves good predictive performance with reduced computational cost by focusing on the most critical variables.
3Device complexity
If model complexity is reduced to decrease computational cost, then memory computation cost is reduced, but model stability deteriorates
Solution Approach 1:
The system optimizes model parameters to achieve the best balance between complexity and stability. By systematically searching through parameter spaces and evaluating models using KS statistics, it identifies parameter settings that maintain model stability even with reduced complexity.
4Reliability
If feature selection is made more conservative with higher cut-off values, then model stability is improved, but the number of selected features decreases
Solution Approach 1:
The system dynamically adjusts feature selection criteria based on user preferences and model performance requirements. Through the dialogue-based interface, users can specify their desired conservatism level, and the system adapts the feature selection process accordingly, allowing flexible trade-offs between the number of features and model stability.
Data Source
AI summary
A method that includes (a) receiving a training dataset, a testing dataset, a number of iterations, and a parameter space of possible parameter values that define a base model, (b) for the number of iterations, performing a parametric search process that produces a report that includes information concerning a plurality of machine learning models, where the parametric search process includes (i) generating a Bayesian optimized parameter space with an option to validate through Stratified Kfold cross validation, where an optimized parameter set includes training data from the training dataset, and testing data from the testing dataset, (ii) running the base model with the final optimized parameter set, thus yielding model results for the plurality of machine learning models, (iii) calculating Kolmogorov-Smirnov (KS) statistics for the model results, and (iv) saving the model results and the KS statistics to the report, and (c) sending the report to a user device.