15 Tips for Bayesian Optimization: A Probabilistic Approach to Hyperparameter Tuning

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Bayesian Optimization is a probabilistic model-based optimization technique that models the objective function as a probability distribution. It builds a surrogate model, such as a Gaussian Process, to approximate the true objective function and uses this model to guide the search for the optimal set of hyperparameters. Below is an example code using the scikit-optimize library for Bayesian Optimization:

I. Bayesian Optimization Process

• Define Hyperparameter Search Space: Specify a range of values for each hyperparameter.
• Define the Objective Function: Create a function that takes hyperparameters as input and returns the objective function value.
• Initialize Bayesian Optimization: Choose a surrogate model (e.g., Gaussian Process) and an acquisition function (e.g., Expected Improvement).
• Optimize: Iteratively sample hyperparameter combinations based on the acquisition function, evaluate the true objective function, and update the surrogate model.
• Select the Best Combination: Identify the combination of hyperparameters that yields the best performance.

II. Example Code

!pip install scikit-optimize…