## ROC Curve

- If we consider all the possible threshold values and the corresponding specificity and sensitivity rate what will be the final model accuracy.
- ROC(Receiver operating characteristic) curve is drawn by taking False positive rate on X-axis and True positive rate on Y- axis.
- ROC tells us, how many mistakes are we making to identify all the positives?

### ROC Curve – Interpretation

- How many mistakes are we making to identify all the positives?
- How many mistakes are we making to identify 70%, 80% and 90% of positives?
- 1-Specificty(false positive rate) gives us an idea on mistakes that we are making
- We would like to make 0% mistakes for identifying 100% positives
- We would like to make very minimal mistakes for identifying maximum positives
- We want that curve to be far away from straight line
- Ideally we want the area under the curve as high as possible

### ROC and AUC

- We want that curve to be far away from straight line. Ideally we want the area under the curve as high as possible
- ROC comes with a connected topic, AUC. Area Under the Curve
- ROC Curve Gives us an idea on the performance of the model under all possible values of threshold.
- We want to make almost 0% mistakes while identifying all the positives, which means we want to see AUC value near to 1

### AUC

- AUC is near to 1 for a good model

## ROC and AUC Calculation

**Building a Logistic Regression Model**

In [10]:

```
###for visualising the plots use matplotlib and import roc_curve,auc from sklearn.metrics
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
%matplotlib inline
actual = Fiber_df[['active_cust']]
false_positive_rate, true_positive_rate, thresholds = roc_curve(actual, predicted_values1)
plt.title('Receiver Operating Characteristic')
plt.plot(false_positive_rate, true_positive_rate)
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.1,1.2])
plt.ylim([-0.1,1.2])
plt.ylabel('True Positive Rate(Sensitivity)')
plt.xlabel('False Positive Rate(Specificity)')
plt.show()
```

In [11]:

```
###Threshold values used for the roc_curve can be viewed from threshold array
thresholds
```

Out[11]:

In [12]:

```
###Area under Curve-AUC
roc_auc = auc(false_positive_rate, true_positive_rate)
roc_auc
```

Out[12]: