Introduction to the Sequential Model-Based Optimization Method
Explore the sequential model-based optimization (SMBO) method and understand how it uses probabilistic models to efficiently select and evaluate hyperparameter combinations. This lesson helps you grasp how SMBO reduces computational resources and time compared to grid and random search methods, enabling better machine learning model performance through more informed hyperparameter tuning.
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What is the sequential model-based optimization method?
Sequential model-based optimization (SMBO) is a powerful method for hyperparameter tuning in ML algorithms. It uses a probabilistic model (surrogate model) to predict the performance of different combinations of hyperparameters based on previously evaluated configurations or combinations. The probabilistic model is then used to suggest the next combination ...