我有一个代码可以提供 SVM 的准确性，但我想知道有多少个是 0 类和 1 类。
这是代码
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
clf = SVC(C=10000.0, kernel='rbf')
t0 = time()
clf.fit(features_train, labels_train)
print "training_time:", round(time()-t0, 3), "s"
t0 = time()
pred = clf.predict(features_test)
print "prediction time:", round(time()-t0, 3), "s"
acc = accuracy_score(pred, labels_test)
print acc
我尝试了下面的代码，但没有成功......
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
clf = SVC(C=10000.0, kernel='rbf', probability=True)
t0 = time()
clf.fit(features_train, labels_train)
print "training_time:", round(time()-t0, 3), "s"
t0 = time()
pred = clf.predict(features_test)
class = clf.predict_proba(features_test)
print sum(class)
print "prediction time:", round(time()-t0, 3), "s"
acc = accuracy_score(pred, labels_test)
print acc
我错过了什么？泰!
最佳答案
您可以创建混淆矩阵来理解您的预测
from sklearn.metrics import confusion_matrix
confusion_matrix(labels_test, pred)
关于python - 如何知道有多少个是0级，有多少个是1级？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/53726503/