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Using Bayesian Optimization for Improving Machine Learning Model Performance
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Conducted research and practical experiments to enhance machine learning model accuracy by applying Bayesian Optimization.
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Utilized Gaussian Processes as surrogate models to efficiently approximate objective functions and implemented acquisition functions to optimize sampling decisions.
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Demonstrated superior model performance with Bayesian Optimization, outperforming traditional methods such as random search and grid search in hyperparameter tuning.
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