How can I use Bayesian optimization for hyperparameter tuning in Python?

How can I use Bayesian optimization for hyperparameter tuning in Python?

You can use libraries like scikit-optimize or GPyOpt to implement Bayesian optimization in Python. Would you like a code example?

Yes, please provide a code example using scikit-optimize.

Sure! Here's a simple example:

from skopt import BayesSearchCV
from sklearn.ensemble import RandomForestClassifier

# Define the model and parameter space
model = RandomForestClassifier()
param_space = {'n_estimators': (10, 100), 'max_depth': (1, 10)}

# Set up Bayesian optimization
opt = BayesSearchCV(model, param_space, n_iter=50)

# Fit the model
opt.fit(X_train, y_train)

This will optimize the n_estimators and max_depth parameters for your Random Forest model.

What are the advantages of using Bayesian optimization over grid search?

Bayesian optimization is more efficient than grid search because it uses past evaluation results to inform future searches, leading to faster convergence on optimal hyperparameters. It also handles noisy evaluations better.

Are there any limitations to Bayesian optimization?

Yes, some limitations include its complexity, the need for a good surrogate model, and it may not perform well with high-dimensional spaces or when the evaluation function is very noisy.