Scikit-学习混淆矩阵 [英] Scikit-learn confusion matrix

问题描述

我不知道我是否正确设置了二进制分类问题.我将正类标记为1，将负类标记为0.但是据我的理解，默认情况下，scikit-learn在其混淆矩阵中将类0用作正类(因此我将其设置为相反).这让我感到困惑.在scikit-learn的默认设置中，第一行是肯定类还是否定类? 让我们假设混淆矩阵输出:

I can't figure out if I've setup my binary classification problem correctly. I labeled the positive class 1 and the negative 0. However It is my understanding that by default scikit-learn uses class 0 as the positive class in its confusion matrix (so the inverse of how I set it up). This is confusing to me. Is the top row, in scikit-learn's default setting, the positive or negative class? Lets assume the confusion matrix output:

confusion_matrix(y_test, preds)
 [ [30  5]
    [2 42] ]

在混乱矩阵中看起来如何?实际实例是scikit-learn中的行还是列?

How would it look like in a confusion matrix? Are the actual instances the rows or the columns in scikit-learn?

          prediction                        prediction
           0       1                          1       0
         -----   -----                      -----   -----
      0 | TN   |  FP        (OR)         1 |  TP  |  FP
actual   -----   -----             actual   -----   -----
      1 | FN   |  TP                     0 |  FN  |  TN

推荐答案

scikit学习按升序对标签进行排序，因此0表示第一列/行，1表示第二列/行

scikit learn sorts labels in ascending order, thus 0's are first column/row and 1's are the second one

>>> from sklearn.metrics import confusion_matrix as cm
>>> y_test = [1, 0, 0]
>>> y_pred = [1, 0, 0]
>>> cm(y_test, y_pred)
array([[2, 0],
       [0, 1]])
>>> y_pred = [4, 0, 0]
>>> y_test = [4, 0, 0]
>>> cm(y_test, y_pred)
array([[2, 0],
       [0, 1]])
>>> y_test = [-2, 0, 0]
>>> y_pred = [-2, 0, 0]
>>> cm(y_test, y_pred)
array([[1, 0],
       [0, 2]])
>>> 

这写在文档中:

labels:数组，形状= [n_classes]，可选 索引矩阵的标签列表.这可用于重新排序或选择标签的子集. 如果未给出，则在y_true或y_pred中至少出现一次的对象将以已排序的顺序使用.

labels : array, shape = [n_classes], optional List of labels to index the matrix. This may be used to reorder or select a subset of labels. If none is given, those that appear at least once in y_true or y_pred are used in sorted order.

因此，您可以通过为confusion_matrix调用提供标签来更改此行为

Thus you can alter this behavior by providing labels to confusion_matrix call

>>> y_test = [1, 0, 0]
>>> y_pred = [1, 0, 0]
>>> cm(y_pred, y_pred)
array([[2, 0],
       [0, 1]])
>>> cm(y_pred, y_pred, labels=[1, 0])
array([[1, 0],
       [0, 2]])

实际/预测的结果与图像中的信息一样累累-预测在列中，实际值在行中

And actual/predicted are oredered just like in your images - predictions are in columns and actual values in rows

>>> y_test = [5, 5, 5, 0, 0, 0]
>>> y_pred = [5, 0, 0, 0, 0, 0]
>>> cm(y_test, y_pred)
array([[3, 0],
       [2, 1]])

• true:0，预测:0(值:3，位置[0，0])
• true:5，预测:0(值:2，位置[1，0])
• true:0，预测:5(值:0，位置[0，1])
• true:5，预测值:5(值:1，位置[1，1])
• true: 0, predicted: 0 (value: 3, position [0, 0])
• true: 5, predicted: 0 (value: 2, position [1, 0])
• true: 0, predicted: 5 (value: 0, position [0, 1])
• true: 5, predicted: 5 (value: 1, position [1, 1])

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