在Python上对8个超参数执行网格搜索

from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC

X = ...
y = ...

model = SVC()
param_grid = {
    'C': [0.1, 1, 10],
    'kernel': ['linear', 'rbf'],
    'gamma': [0.1, 1, 10],
    'degree': [2, 3, 4],
    'coef0': [0.0, 0.5, 1.0],
    'shrinking': [True, False],
    'probability': [True, False],
    'tol': [0.001, 0.01, 0.1]
}

grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5)
grid_search.fit(X, y)

best_params = grid_search.best_params_
best_score = grid_search.best_score_

print("Best Parameters: ", best_params)
print("Best Score: ", best_score)