Advantages and Disadvantages of the SMBO Method

Advantages of the SMBO method

• Efficient use of resources: SMBO selects the most promising set of hyperparameters to evaluate the machine learning model and then updates its internal model (probabilistic model) based on the results. This results in more efficient use of computational resources because the model is not evaluated on hyperparameters that are unlikely to produce the best result.

• Can handle constraints: Many ML problems have some limitations on the hyperparameters, such as a limit on the search space or a maximum number of iterations. SMBO can handle these constraints by incorporating them into its internal model and selecting values of hyperparameters that satisfy the constraints.

• Provide balance between exploration and exploitation: SMBO uses a probabilistic model to balance the discovery of novel hyperparameters and the exploitation of those that show promise to produce a good performance. This makes it possible to search the hyperparameter space more thoroughly, preventing the algorithm from becoming stuck in the suboptimal region.

• Automated tuning: SMBO automates the process of hyperparameter optimization. This task can be time-consuming and requires domain expertise. SMBO requires the search space for selected hyperparameters and the definition of the objective function, and then the algorithm handles the rest.

Disadvantages of the SMBO method

• Requires a good initial set of hyperparameters: SMBO primarily relies on the initial set of hyperparameters, which might be difficult if the user (data scientist) has a limited prior understanding of the problem. If the initial combination of hyperparameters is not properly selected, SMBO can get stuck in local optima, resulting in poor performance.

• May get trapped in local minima: SMBO is not guaranteed to find the global optimum of the objective function; instead, it may converge to a local minimum. This can be an issue if the local minimum is very different from the global minimum, and it could lead to the performance of the model being less than optimal.