gradientboostingclassifier sklearn in python all possible hyperparameters in python

To use Gradient Boosting Classifier in sklearn with all possible hyperparameters, you can use the GridSearchCV function from sklearn as follows:

main.py
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV

# define the model
gb = GradientBoostingClassifier()

# define the parameters for grid search
params = {
    "n_estimators": [100, 200, 300],
    "max_depth": [3, 5, 7, 9],
    "learning_rate": [0.01, 0.1, 1],
    "subsample": [0.5, 0.8, 1],
    "min_samples_split": [2, 5, 10],
}

# create the grid search object
grid_search = GridSearchCV(gb, params, cv=5)

# fit the model to the data
grid_search.fit(X_train, y_train)

# display the best hyperparameters
print(grid_search.best_params_)

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In this example, GridSearchCV is used to search through a range of hyperparameters for GradientBoostingClassifier, including n_estimators, max_depth, learning_rate, subsample, and min_samples_split. The cv parameter in GridSearchCV determines the number of folds used in the cross-validation process. The best hyperparameters are then displayed with grid_search.best_params_.

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related categories

sklearn
gradient-boosting-classifier
hyperparameters

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