我可以在scikit-learn中获得错误预测的列表吗?

问题描述

我们可以使用svm.SVC.score()来评估SVM模型的准确性.如果预测错误，我想获得预测的班级和实际的班级.如何在scikit-learn中实现这一目标?

We can use svm.SVC.score() to evaluate the accuracy of the SVM model. I want to get the predicted class and the actual class in case of wrong predictions. How can I achieve this in scikit-learn?

推荐答案

最简单的方法是遍历您的预测(以及正确的分类)，并对输出进行任何操作(在下面的示例中，我将仅打印该输出)到标准输出).

The simplest approach is just to iterate over your predictions (and correct classifications) and do whatever you want with the output (in the following example I will just print it to stdout).

让我们假设您的数据位于输入，标签中，而您训练有素的SVM位于clf中，那么您就可以这样做

Lets assume that your data is in inputs, labels, and your trained SVM is in clf, then you can just do

predictions = clf.predict(inputs)
for input, prediction, label in zip(inputs, predictions, labels):
  if prediction != label:
    print(input, 'has been classified as ', prediction, 'and should be ', label)

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