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Answer: Employ a Bayesian optimization search algorithm with an adaptive number of trials based on early performance metrics.
For efficient and effective hyperparameter tuning in a single-node model, using a Bayesian optimization search algorithm in Hyperopt is recommended. This approach allows for an adaptive number of trials based on early performance metrics, optimizing both the efficiency and the effectiveness of the tuning process.
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Given a dataset and a requirement to tune hyperparameters for a single-node model using Hyperopt, outline the steps you would take to ensure that the tuning process is both efficient and effective. Include considerations for the choice of search algorithm and the impact of the number of trials on the final model performance.
A
Use a random search algorithm with a high number of trials to ensure comprehensive coverage of the hyperparameter space.
B
Employ a Bayesian optimization search algorithm with an adaptive number of trials based on early performance metrics.
C
Sequential model-based optimization is not suitable for single-node models and should be avoided.
D
Always use the default settings in Hyperopt without adjusting the search algorithm or the number of trials.
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