我正在使用以下代码进行准确度得分计算。为什么默认配置比 GridSearch 提供更好的结果？

默认配置

clf = svm.SVC(kernel='rbf', gamma='auto')               
clf.fit(x_train, y_train.values.ravel())                

y_train_pred = clf.predict(x_train)             
y_test_pred = clf.predict(x_test)               

print('Train set accuracy: '+'{}'.format(metrics.accuracy_score(y_train, y_train_pred)))                
print('Test set accuracy: '+'{}'.format(metrics.accuracy_score(y_test, y_test_pred)))   

训练集精度：0.861101243339254
测试集精度：0.8480113636363636

网格搜索配置

param_grid = {'C': (0.001, 0.01, 0.1, 1, 10),
              'kernel': ('linear', 'poly', 'rbf', 'sigmoid'),
              'class_weight': ('balanced', None),
              'gamma' : ('scale', 'auto'),
              'shrinking': (True, False)}

grid_search = GridSearchCV(svm.SVC(gamma='scale'), param_grid, cv=5)
grid_results = grid_search.fit(x_train, y_train.values.ravel())

print(grid_results.best_score_)
print(grid_results.best_estimator_)
print(grid_results.best_params_)

0.8373001776198934
SVC（C=1，cache_size=200，class_weight=None，coef0=0.0，decision_function_shape='ovr'，degree=3，gamma='auto'，kernel='rbf'，max_iter=-1，probability=False， random_state=None, shrinking=True, tol=0.001, verbose=False)
{'C': 1, 'class_weight': None, 'gamma': 'auto', 'kernel': 'rbf', 'shrinking': True }