Introduction to the Sequential Model-Based Optimization Method

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 of hyperparameters to evaluate the performance of the ML model and determine whether it produces better results or not.

Note: This process is called sequential because each new combination of hyperparameters is selected based on the results of the previous evaluation.

The goal of this method is to cut down on ...