我想在 svm 上获得具有最佳参数的预测结果，但我没有找到获得它的方法。如何获得 K 折的预测结果？

from __future__ import print_function

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
from time import *
from sklearn import metrics
X=datascaled.iloc[:,0:13]
y=datascaled['num']

np.random.seed(1)
# Split the dataset in two equal parts
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=0)

# Set the parameters by cross-validation
tuned_parameters =  [{'kernel': ['rbf'], 'gamma': [1e-2, 1e-3, 1e-4, 1e-5],
                     'C': [0.001, 0.10, 0.1, 10, 25, 50, 100, 1000]},
                    {'kernel': ['sigmoid'], 'gamma': [1e-2, 1e-3, 1e-4, 1e-5],
                     'C': [0.001, 0.10, 0.1, 10, 25, 50, 100, 1000] },{'kernel': ['linear'], 'C': [0.001, 0.10, 0.1, 10, 25, 50, 100, 1000]}]              





print()

clf = GridSearchCV(SVC(), tuned_parameters, cv=10,
                       scoring='accuracy')
t0 = time()

svmclf=clf.fit(X_train, y_train)
t = time() - t0
print("Best parameters set found on development set:")
print()
print(clf.best_params_)
print()
print('Training accuracy')
print(clf.best_score_)