gridsearch for gradientboostingclassifier sklearn in python

main.py
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

# Generate some random data for demonstration
X, y = make_classification()

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

# Define the parameter grid to search over
param_grid = {
    'n_estimators': [50, 100, 200],
    'learning_rate': [0.05, 0.1, 0.2],
    'max_depth': [3, 4, 5]
}

# Initialize the GradientBoostingClassifier model
clf = GradientBoostingClassifier()

# Initialize the grid search object
grid_search = GridSearchCV(clf, param_grid=param_grid, cv=5, n_jobs=-1)

# Fit the grid search object to the data
grid_search.fit(X_train, y_train)

# Print the best parameters and accuracy score found during the grid search
print("Best parameters: ", grid_search.best_params_)
print("Best accuracy score: {:.3f}".format(grid_search.best_score_))
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