This paper presents a practical Bayesian optimization approach for tuning hyperparameters in machine learning algorithms. The authors propose using Gaussian processes (GPs) to model the generalization performance of a learning algorithm as a sample from a GP, allowing for efficient use of information gathered from previous experiments. They show that thoughtful choices in the GP prior and inference procedure can significantly impact the success of Bayesian optimization. The authors also describe new algorithms that account for the variable cost (duration) of learning experiments and can leverage parallelism across multiple cores or machines. The paper introduces a fully Bayesian treatment of GP kernel parameters, which is critical for robust results, and examines the impact of the kernel itself. They propose a new algorithm that accounts for cost in experiments and another that can take advantage of parallelism. These algorithms improve upon previous automatic procedures and can reach or surpass human expert-level optimization on a diverse set of algorithms, including latent Dirichlet allocation, structured SVMs, and convolutional neural networks. The authors also discuss practical considerations for Bayesian optimization of hyperparameters, including the choice of covariance functions and their hyperparameters, modeling costs, and parallelizing Bayesian optimization. They show that integrating over hyperparameters is superior to using point estimates and that parallelized Bayesian optimization can significantly reduce the time required to find optimal parameters. Empirical analyses on several challenging machine learning problems, including the Branin-Hoo function, logistic regression, online LDA, motif finding with structured SVMs, and convolutional networks on CIFAR-10, demonstrate the effectiveness of their approach. The results show that their Bayesian optimization methods outperform existing strategies and human experts on these tasks, achieving better performance with fewer function evaluations and less computational time. The authors conclude that their methods provide a significant improvement in hyperparameter tuning for machine learning algorithms.This paper presents a practical Bayesian optimization approach for tuning hyperparameters in machine learning algorithms. The authors propose using Gaussian processes (GPs) to model the generalization performance of a learning algorithm as a sample from a GP, allowing for efficient use of information gathered from previous experiments. They show that thoughtful choices in the GP prior and inference procedure can significantly impact the success of Bayesian optimization. The authors also describe new algorithms that account for the variable cost (duration) of learning experiments and can leverage parallelism across multiple cores or machines. The paper introduces a fully Bayesian treatment of GP kernel parameters, which is critical for robust results, and examines the impact of the kernel itself. They propose a new algorithm that accounts for cost in experiments and another that can take advantage of parallelism. These algorithms improve upon previous automatic procedures and can reach or surpass human expert-level optimization on a diverse set of algorithms, including latent Dirichlet allocation, structured SVMs, and convolutional neural networks. The authors also discuss practical considerations for Bayesian optimization of hyperparameters, including the choice of covariance functions and their hyperparameters, modeling costs, and parallelizing Bayesian optimization. They show that integrating over hyperparameters is superior to using point estimates and that parallelized Bayesian optimization can significantly reduce the time required to find optimal parameters. Empirical analyses on several challenging machine learning problems, including the Branin-Hoo function, logistic regression, online LDA, motif finding with structured SVMs, and convolutional networks on CIFAR-10, demonstrate the effectiveness of their approach. The results show that their Bayesian optimization methods outperform existing strategies and human experts on these tasks, achieving better performance with fewer function evaluations and less computational time. The authors conclude that their methods provide a significant improvement in hyperparameter tuning for machine learning algorithms.