204.2.4 Goodness of fit for Logistic Regression
Link to the previous post :https://statinfer.com/204-2-3-multiple-logistic-regression/
Goodness of Fit for a 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.
- Classification Matrix
- AIC and BIC
- ROC & AUC
Out of these three we will go through Classification matrix or confusion matrix in this post and understand rest in upcoming posts.
Classification Table & Accuracy
| Predicted / Actual | 0 | 1 |
|---|---|---|
| 0 | True Positive (TP) | False Positive (FP) |
| 1 | False Negative (FN) | True Negative (TN) |
- Also known as confusion matrix
- Accuracy=(TP+TN)/(TP+FP+FN+TN)
Practice : Confusion Matrix & Accuracy
- Create confusion matrix for Fiber bits model(Model built in previous posts of this series)
In [25]:
###for using confusion matrix###
from sklearn.metrics import confusion_matrix
cm1 = confusion_matrix(Fiber[['active_cust']],predict1)
print(cm1)
- Find the accuracy value for fiber bits model
In [26]:
total1=sum(sum(cm1))
accuracy1=(cm1[0,0]+cm1[1,1])/total1
accuracy1
Out[26]:
