confusion matrix_the confusion matrices

confusion matrix

https://scikit-learn.org/stable/modules/model_evaluation.html

confusion [kən'fjuːʒ(ə)n]：n. 混淆，混乱，困惑
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The confusion_matrix function evaluates classification accuracy by computing the confusion matrix with each row corresponding to the true class (Wikipedia and other references may use different convention for axes.)
confusion_matrix 函数通过计算混淆矩阵来评估分类准确性，每个行对应于真实类别 (维基百科和其他引用可以使用不同的轴约定。)

By definition, entry $i,j$ in a confusion matrix is the number of observations actually in group $i$, but predicted to be in group $j$. Here is an example:
根据定义，混淆矩阵中的条目 $i,j$ 是实际在 $i$ 组中的观察数，但预计在 $j$ 组中。Here is an example:

>>> from sklearn.metrics import confusion_matrix
>>> y_true = [2, 0, 2, 2, 0, 1]
>>> y_pred = [0, 0, 2, 2, 0, 2]
>>> confusion_matrix(y_true, y_pred)
array([[2, 0, 0],
       [0, 0, 1],
       [1, 0, 2]])
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#!/usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from sklearn.metrics import confusion_matrix

y_true = [2, 0, 2, 2, 0, 1]
y_pred = [0, 0, 2, 2, 0, 2]
print(confusion_matrix(y_true, y_pred))
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strong@foreverstrong:~/git_workspace/MonoGRNet$ python yongqiang.py 
[[2 0 0]
 [0 0 1]
 [1 0 2]]
strong@foreverstrong:~/git_workspace/MonoGRNet$
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Here is a visual representation of such a confusion matrix (this figure comes from the Confusion matrix example):

For binary problems, we can get counts of true negatives, false positives, false negatives and true positives as follows:
对于二元问题，我们可以得到真阴性，误报，假阴性和真阳性的计数如下：

>>> y_true = [0, 0, 0, 1, 1, 1, 1, 1]
>>> y_pred = [0, 1, 0, 1, 0, 1, 0, 1]
>>> tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
>>> tn, fp, fn, tp
(2, 1, 2, 3)
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References

https://en.wikipedia.org/wiki/Confusion_matrix