What is Bayesian Optimization?
Bayesian Optimization is a global optimization technique for expensive black-box functions that uses Bayesian models to approximate the objective function. It is particularly useful for optimizing complex, non-linear, and noisy objective functions with a limited number of function evaluations. Bayesian Optimization is widely used in hyperparameter tuning, experimental design, and other applications where evaluating the objective function is computationally expensive or time-consuming.
How does Bayesian Optimization work?
Bayesian Optimization works by constructing a probabilistic model of the objective function and then using an acquisition function to decide where to sample next based on the current model. The most common model used in Bayesian Optimization is the Gaussian Process (GP), which provides a flexible and interpretable model for the objective function.
The acquisition function balances exploration (sampling regions where the model is uncertain) and exploitation (sampling regions where the model predicts high values) to guide the optimization process. Common acquisition functions include Expected Improvement (EI), Probability of Improvement (PI), and Upper Confidence Bound (UCB).
Example of Bayesian Optimization in Python using Scikit-Optimize
import numpy as np
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
from skopt import BayesSearchCV
# Load the Iris dataset
iris = load_iris()
# Define the search space for hyperparameters
search_space = {
"n_estimators": (10, 200),
"max_depth": (1, 50),
"min_samples_split": (2, 20),
"min_samples_leaf": (1, 20),
}
# Initialize the Bayesian optimization
bayes_opt = BayesSearchCV(
RandomForestClassifier(),
search_spaces=search_space,
n_iter=50,
cv=5,
n_jobs=-1,
random_state=42,
)
# Perform the Bayesian optimization
bayes_opt.fit(iris.data, iris.target)
# Print the best hyperparameters
print("Best hyperparameters:", bayes_opt.best_params_)
