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Updated: Nov 11, 2025

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
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Parameter Sensitivity Analysis for the Progressive Sampling-Based Bayesian Optimization Method for Automated Machine

Weipeng Zhou1, Gang Luo1

  • 1University of Washington, Seattle, WA 98195, USA.

Heterogenous Data Management, Polystores, and Analytics for Healthcare : VLDB Workshops, Poly 2020 and DMAH 2020 Virtual Event, August 31 and September 4, 2020 : Revised Selected Papers
|March 26, 2021
PubMed
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This summary is machine-generated.

Automated machine learning model selection is crucial for real-world problems. Our progressive sampling-based Bayesian optimization (PSBO) method

Keywords:
Automated machine learning model selectionBayesian optimizationProgressive samplingSensitivity analysis

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Automated machine learning (AutoML) model selection is essential for applying ML to real-world problems.
  • Existing AutoML methods struggle with large datasets due to inefficiency.
  • Progressive sampling-based Bayesian optimization (PSBO) was developed to address these limitations.

Purpose of the Study:

  • To analyze the sensitivity of the 20 parameters within the PSBO method.
  • To determine the room for improvement over default parameter values.
  • To identify the safe operating ranges for each parameter.

Main Methods:

  • Sensitivity analysis was performed on the 20 parameters of the PSBO method.
  • The impact of each parameter on PSBO performance was evaluated.
  • Default parameter values were used as a baseline for comparison.

Main Results:

  • The default values for the 20 PSBO parameters generally perform well.
  • Limited room for improvement exists by adjusting parameters beyond their defaults.
  • Each parameter demonstrates a substantial safe range, indicating robustness to value variations.

Conclusions:

  • The default parameter settings for the PSBO method are effective and robust.
  • The PSBO method's performance is not highly sensitive to individual parameter adjustments within their safe ranges.
  • This suggests the PSBO method is reliable for automated machine learning model selection without extensive hyperparameter tuning.