学ぶ Bayesian Optimization with skopt | Hyperparameter Optimization

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Bayesian optimization is a powerful approach for hyperparameter tuning in AutoML. Unlike grid search and random search, which explore hyperparameter spaces without considering past results, Bayesian optimization builds a probabilistic model of the objective function. This model predicts how different hyperparameter combinations will perform and uses that information to select the most promising parameters to try next. By focusing the search on areas likely to yield better results, Bayesian optimization often finds optimal settings with fewer experiments, saving both time and computational resources. This is especially valuable when training models is expensive or time-consuming.


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from skopt import BayesSearchCV
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split

# Load data
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

# Define the search space for DecisionTreeClassifier
search_space = {
    'max_depth': (1, 10),
    'min_samples_split': (2, 20),
}

# Set up Bayesian optimization with BayesSearchCV
opt = BayesSearchCV(
    DecisionTreeClassifier(),
    search_spaces=search_space,
    n_iter=20,
    cv=3,
    random_state=42
)

# Run optimization
opt.fit(X_train, y_train)

# Print best parameters
print("Best parameters:", opt.best_params_)
print("Best cross-validation score: {:.3f}".format(opt.best_score_))

Note

Use bayesian optimization when training your models is computationally expensive or time-consuming. It is more efficient than grid or random search in such scenarios.

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