sklearn.metrics.confusion_matrix

sklearn.metrics.confusion_matrix

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html

confusion [kən'fjuːʒ(ə)n]：n. 混淆，混乱，困惑

混淆矩阵是一个误差矩阵，用来可视化地评估监督学习算法的性能。混淆矩阵大小为 (n_classes, n_classes) 的方阵，其中 n_classes 表示类的数量。通过混淆矩阵，可以很容易看出系统是否会弄混两个类，这也是混淆矩阵名字的由来。

混淆矩阵是一种特殊类型的列联表 (contingency table) 或交叉制表 (cross tabulation or crosstab)。其有两维 (真实值和预测值)，这两维都具有相同的类 (classes) 的集合。

sklearn 和 TensorFlow 中有 API。

1. sklearn.metrics.confusion_matrix(y_true, y_pred, labels=None, sample_weight=None)

Compute confusion matrix to evaluate the accuracy of a classification.
计算混淆矩阵以评估分类的准确性。

By definition a confusion matrix $C$ is such that $C_{i,j}$ is equal to the number of observations known to be in group $i$ but predicted to be in group $j$.
根据定义，混淆矩阵 $C$ 是这样的，$C_{i,j}$ 等于已知在 $i$ 组中但预测在 $j$ 组中的观察数。

Thus in binary classification, the count of true negatives is $C_{0,0}$, false negatives is $C_{1,0}$, true positives is $C_{1,1}$ and false positives is $C_{0,1}$.
因此，在二进制分类中，真阴性的计数是 $C_{0,0}$，假阴性是 $C_{1,0}$，真阳性是 $C_{1,1}$，误报是 $C_{0,1}$。

1.1 Parameters

y_true : array, shape = [n_samples]
Ground truth (correct) target values.

y_pred : array, shape = [n_samples]
Estimated targets as returned by a classifier.

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.
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 labels 为 None，那么在 y_true 或 y_pred 中至少出现一次的那些按排序顺序使用。

sample_weight : array-like of shape = [n_samples], optional
Sample weights.

1.2 Returns

C : array, shape = [n_classes, n_classes]
Confusion matrix.

2. Examples

>>> 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]])

>>> y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
>>> y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
>>> confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"])
array([[2, 0, 0],
       [0, 0, 1],
       [1, 0, 2]])

In the binary case, we can extract true positives, etc as follows:

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

References

https://en.wikipedia.org/wiki/Confusion_matrix
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html