为什么scikit learn SVM分类器交叉验证如此缓慢?

我试图比较我拥有的数据集上的多个分类器。为了获得分类器的准确分数,我现在对每个分类器执行10倍交叉验证。除SVM(线性核和rbf核)外,所有这些方法都很好。数据加载方式如下:

dataset = pd.read_csv("data/distance_annotated_indels.txt", delimiter="\t", header=None)

X = dataset.iloc[:, [5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]].values
y = dataset.iloc[:, 4].values

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

交叉验证(例如随机林)效果良好:

start = time.time()
classifier = RandomForestClassifier(n_estimators = 100, criterion = 'entropy')
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
cv = ShuffleSplit(n_splits=10, test_size=0.2)
scores = cross_val_score(classifier, X, y, cv=10)
print(classification_report(y_test, y_pred))
print("Random Forest accuracy after 10 fold CV: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2) + ", " + str(round(time.time() - start, 3)) + "s")

输出:

             precision    recall  f1-score   support

          0       0.97      0.95      0.96      3427
          1       0.95      0.97      0.96      3417

avg / total       0.96      0.96      0.96      6844

Random Forest accuracy after 10 fold CV: 0.92 (+/- 0.06), 90.842s

然而,对于SVM来说,这个过程需要很多时间(等待了2个小时,仍然没有结果)。sklearn网站并没有让我变得更聪明。我是否应该为SVM分类器做些不同的事情?SVM代码如下:

start = time.time()
classifier = SVC(kernel = 'linear')
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
scores = cross_val_score(classifier, X, y, cv=10)
print(classification_report(y_test, y_pred))
print("Linear SVM accuracy after 10 fold CV: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2) + ", " + str(round(time.time() - start, 3)) + "s")