Link to the previous post :https://statinfer.com/204-2-3-multiple-logistic-regression/
There are quite a lot of methods to find how good a model is. However, we will stick to the most important measures applicable for a logistic regression model.
Out of these three we will go through Classification matrix or confusion matrix in this post and understand rest in upcoming posts.
Predicted / Actual | 0 | 1 |
---|---|---|
0 | True Positive (TP) | False Positive (FP) |
1 | False Negative (FN) | True Negative (TN) |
###for using confusion matrix###
from sklearn.metrics import confusion_matrix
cm1 = confusion_matrix(Fiber[['active_cust']],predict1)
print(cm1)
total1=sum(sum(cm1))
accuracy1=(cm1[0,0]+cm1[1,1])/total1
accuracy1