Hyperparameter Tuning Using Bayesian Optimization

In this project, we'll delve into the realm of hyperparameter tuning using Dragonfly, a powerful Python library for efficient Bayesian optimization. Hyperparameter tuning is a critical step in the process of building machine learning models because it involves finding the optimal set of hyperparameters that maximize the model’s performance on the validation data. However, the search space of hyperparameters can be vast, and evaluating each combination can be computationally expensive and time-consuming.

Dragonfly comes to the rescue with its Bayesian optimization capabilities. Bayesian optimization is a sequential model-based optimization (SMBO) technique that leverages probabilistic models—usually Gaussian processes (GPs)—to model the objective function’s behavior and guide the search toward promising regions of the hyperparameter space. As a result, Bayesian optimization efficiently explores the hyperparameter space with fewer evaluations and converges to the optimal solution faster than grid search or random search.

In this project, we'll create a Python script that demonstrates how to use Dragonfly for hyperparameter tuning and compare its performance with other optimization techniques. We'll be working with a hypothetical machine learning task and a corresponding dataset for demonstration purposes.