热图上 FN 和 FP 之间的混淆矩阵混淆

Question

I'm trying to solve a classification problem with machine learning on python. The topic is about using credit dataset to predict if the person has a good or bad credit. When a person has a good credit then 0, if not then 1. I created a confusion matrix with LR. I'm not sure if 13 is FN or FP. Could anyone clarify this for me please? This he confusion matrix

Answer 1

It's a bit weird to make 0 your positive class. In any case, you need to flip your confusion matrix. Let's say your test and predict are such

y_test = np.repeat([0,1,0,1],[128,34,13,25])
y_pred = np.repeat([0,0,1,1],[128,34,13,25])

We always do prediction, actual:

from sklearn.metrics import confusion_matrix
import seaborn as sns
cfm = confusion_matrix(y_pred,y_test)
sns.heatmap(cfm,annot=True,cmap="Blues")

So in this case, we just go on with zero as your positive class it's exactly like what you have in this diagram from wiki for confusion matrix :

The top right is false positive (34) and your bottom left is false negative.