Python 如何获得分类器在 sklearn 中进行预测的置信度？
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How to get a classifier's confidence score for a prediction in sklearn?
提问by user3377126
I would like to get a confidence score of each of the predictions that it makes, showing on how sure the classifier is on its prediction that it is correct.
我想获得它所做的每个预测的置信度分数，以显示分类器对其正确性的预测有多确定。
I want something like this:
我想要这样的东西：
How sure is the classifier on its prediction?
分类器的预测有多确定？
Class 1: 81% that this is class 1
Class 2: 10%
Class 3: 6%
Class 4: 3%
Class 1: 81% 这是Class 1
Class 2: 10%
Class 3: 6%
Class 4: 3%
Samples of my code:
我的代码示例：
features_train, features_test, labels_train, labels_test = cross_validation.train_test_split(main, target, test_size = 0.4)
# Determine amount of time to train
t0 = time()
model = SVC()
#model = SVC(kernel='poly')
#model = GaussianNB()
model.fit(features_train, labels_train)
print 'training time: ', round(time()-t0, 3), 's'
# Determine amount of time to predict
t1 = time()
pred = model.predict(features_test)
print 'predicting time: ', round(time()-t1, 3), 's'
accuracy = accuracy_score(labels_test, pred)
print 'Confusion Matrix: '
print confusion_matrix(labels_test, pred)
# Accuracy in the 0.9333, 9.6667, 1.0 range
print accuracy
model.predict(sub_main)
# Determine amount of time to predict
t1 = time()
pred = model.predict(sub_main)
print 'predicting time: ', round(time()-t1, 3), 's'
print ''
print 'Prediction: '
print pred
I suspect that I would use the score() function, but I seem to keep implementing it correctly. I don't know if that's the right function or not, but how would one get the confidence percentage of a classifier's prediction?
我怀疑我会使用 score() 函数，但我似乎一直在正确地实现它。我不知道这是否是正确的函数，但是如何获得分类器预测的置信百分比？
采纳答案by Justin Peel
Per the SVC documentation, it looks like you need to change how you construct the SVC:
根据SVC 文档，您似乎需要更改构建 SVC 的方式：
model = SVC(probability=True)
and then use the predict_proba method:
然后使用 predict_proba 方法：
class_probabilities = model.predict_proba(sub_main)
回答by Jianxun Li
For those estimators implementing predict_proba()method, like Justin Peel suggested, You can just use predict_proba()to produce probability on your prediction.
对于那些实施predict_proba()方法的估计器，就像 Justin Peel 建议的那样，您可以使用它predict_proba()来产生预测的概率。
For those estimators which do not implement predict_proba()method, you can construct confidence interval by yourself using bootstrap concept (repeatedly calculate your point estimates in many sub-samples).
对于那些没有实现predict_proba()方法的估计量，您可以使用 bootstrap 概念自行构建置信区间（在许多子样本中重复计算您的点估计）。
Let me know if you need any detailed examples to demonstrate either of these two cases.
如果您需要任何详细示例来演示这两种情况，请告诉我。
