Print ROC AUC Receiver Operating Characteristic Area Under Curve

The receiver operating characteristic area under curve is a way to measure the performance of a classification model, may be created using algorithms like Logistic Regression.

ROC-AUC is basically a graph where we plot true positive rate on y-axis and false positive rate on x-axis.

If a model is good the AUC will be close to 1.

Area Under Curve measures the performance of model better than accuracy because ROC-AUC does not depend on size of test data. 

Note that TPR and FPR are defined as below in the context of getting Confusion Matrix plotted.

• True Positive Rate or TPR = It is number of correct positive predictions divided by the total number of positives = TP/(TP+FN)
• False Positive Rate or FPR = It is number of incorrect positive predictions divided by total number of negatives = FP/(TN+FP)

The ROC-AUC plot is used to visualize and represent the performance of the classifier model.

We can print the receiver operating characteristic area under curve using scikit-learn by first importing below libraries.

import matplotlib.pyplot as pyplt
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
from sklearn.metrics import auc

Then we need to use below code to print it.

FPR, TPR, thresholds = roc_curve(y_test, y_pred)
ROC_AUC = auc(FPR, TPR)
pyplt.figure(figsize=(6,6))
pyplt.plot(FPR, TPR, color=’black’, label=’AUC = %0.2f’ % ROC_AUC)
pyplt.plot([0, 1], [0, 1], color=’red’, linestyle=’-‘)
pyplt.xlabel(‘False Positive Rate’)
pyplt.ylabel(‘True Positive Rate’)
pyplt.title(‘ROC curve’)
pyplt.legend(loc=”upper left”)
pyplt.xlim([0.0, 1.0])
pyplt.ylim([0.0, 1.0])
pyplt.show()

This code plot below ROC AUC

You can also print the Area Under Curve using below code

roc_auc_score(y_test, y_pred) 

0.7610977872301914

Note: You can also visualize or print confusion matrix and machine learning classification report

I hope you enjoyed this article and can start using some of the techniques described here in your own projects soon.