## Goodness of Fit for a Logistic Regression

There are quite a lot methods to find how good a model is. However, we will stick to 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]: