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Using Bayesian Optimization for Improving Machine Learning Model Performance

  • Conducted research and practical experiments to enhance machine learning model accuracy by applying Bayesian Optimization.

  • Utilized Gaussian Processes as surrogate models to efficiently approximate objective functions and implemented acquisition functions to optimize sampling decisions.

  • Demonstrated superior model performance with Bayesian Optimization, outperforming traditional methods such as random search and grid search in hyperparameter tuning.

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Key Components: Bayesian Optimization, Hyperparameter Tuning, Machine Learning, Surrogate Model, Acquisition Function. Gaussian Processes, Algorithm Enhancement, Python, scikit-learn

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