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# 204.2.4 Goodness of fit for Logistic Regression

##### How good our model is?
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)

[[29210 12931]
[10183 47676]]

• 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]:
0.76885999999999999

The next post is about multicollinearity and individual impact of variables in logistic regression.
Link to the next post : https://statinfer.com/204-2-5-multicollinearity-and-individual-impact-of-variables-in-logistic-regression/
21st June 2017

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