我正在一个简单的循环中挣扎：
for kernel in ('linear','poly', 'rbf'):
svm = svm.SVC(kernel=kernel, C=1)
svm.fit(trainingdata_without_labels, trainingdata_labels)
predicted_labels = svm.predict(testdata_without_labels)
print("testing success ratio with "+ kernel + "kernel :" + str(accuracy_score(testdata_labels, predicted_labels)))
它在第一个循环中工作正常，但随后我得到：
AttributeError：“ SVC”对象没有属性“ SVC”
我真的很想了解我的错误。
在此先感谢<3
您正在用第一个循环覆盖svm。
尝试更改分类器的名称，例如：
for kernel in ('linear','poly', 'rbf'):
classifier_svm = svm.SVC(kernel=kernel, C=1)
classifier_svm.fit(trainingdata_without_labels, trainingdata_labels)
predicted_labels = classifier_svm.predict(testdata_without_labels)
print("testing success ratio with "+ kernel + "kernel :" + str(accuracy_score(testdata_labels, predicted_labels)))
此外，我认为您尝试做的事情是找到最佳内核，使用GridSearchCV可以更轻松地解决该问题：
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.svm import SVC
tuned_parameters = [{'kernel': ['linear', 'poly', 'rbf'],
'C': [1]}
]
clf = GridSearchCV(SVC(), tuned_parameters, scoring='accuracy')
clf.fit(trainingdata_without_labels, trainingdata_labels)
print("Best parameters set found on development set:\n")
print(clf.best_params_)
print("\nGrid scores on development set:\n")
means = clf.cv_results_['mean_test_score']
stds = clf.cv_results_['std_test_score']
for mean, std, params in zip(means, stds, clf.cv_results_['params']):
print("%0.3f (+/-%0.03f) for %r"
% (mean, std * 2, params))
print("\nDetailed classification report:\n")
print("The model is trained on the full development set.")
print("The scores are computed on the full evaluation set.")
y_true, y_pred = testdata_labels, clf.predict(testdata_without_labels)
print(classification_report(y_true, y_pred))
使用此代码段，您将使用3个内核训练模型，并进行5倍交叉验证。最后计算测试变量的分类报告（精确度，召回率，f1-分数）。最终报告应如下所示（每行将是一个可在您的数据中进行预测的类）：
precision recall f1-score support
0 1.00 1.00 1.00 27
1 0.95 1.00 0.97 35
2 1.00 1.00 1.00 36
3 1.00 1.00 1.00 29
4 1.00 1.00 1.00 30
5 0.97 0.97 0.97 40
6 1.00 0.98 0.99 44
7 1.00 1.00 1.00 39
8 1.00 0.97 0.99 39
9 0.98 0.98 0.98 41
accuracy 0.99 360
macro avg 0.99 0.99 0.99 360
weighted avg 0.99 0.99 0.99 360
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