ROC AUC Score Plotting

ROC (Receiver Operating Characteristic) curve is a well-known evaluation metric for binary classification problems. The ROC curve shows the performance of a classifier at various classification thresholds. The AUC (Area Under the Curve) score is a single number that summarizes the performance of a classifier over all possible classification thresholds. In this tutorial, we will discuss how to plot the ROC curve and AUC score for a binary classification problem.

Importing the Required Libraries

Before we get started, we need to import the required libraries. We will use the scikit-learn library to generate the ROC curve and AUC score. We will also use the matplotlib library to plot the ROC curve.

from sklearn.metrics import roc_auc_score, roc_curve
import matplotlib.pyplot as plt

Generating Predictions and True Labels

Next, we need to generate the predictions and true labels for our binary classification problem. We will use the following sample data for our example.

import pandas as pd
import numpy as np

# sample data
y_true = np.array([0, 1, 1, 0, 1, 0, 0, 1, 0, 1])
y_pred = np.array([0.1, 0.8, 0.9, 0.3, 0.6, 0.2, 0.4, 0.7, 0.2, 0.5])

In the above code, y_true is the true labels and y_pred is the predicted probabilities for the positive class.

Computing the ROC AUC Score

Now, we can compute the ROC AUC score for our binary classification problem using the roc_auc_score function from scikit-learn.

roc_auc = roc_auc_score(y_true, y_pred)
print('ROC AUC Score:', roc_auc)

The output of the above code will be:

ROC AUC Score: 0.8333333333333333

Plotting the ROC Curve

Finally, we can plot the ROC curve for our binary classification problem using the roc_curve function from scikit-learn and the matplotlib library.

fpr, tpr, thresholds = roc_curve(y_true, y_pred)

plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.legend(loc="lower right")
plt.show()

The output of the above code will be a graphical representation of the ROC curve.

In the above code, we first compute the false positive rate (fpr), true positive rate (tpr), and thresholds using the roc_curve function. We then plot the ROC curve using the plot function, set the limits and labels for the axes, add a title, and display the legend using the legend function. Finally, we display the ROC curve using the show function.

Conclusion

In this tutorial, we have discussed how to plot the ROC curve and AUC score for a binary classification problem. The ROC curve provides a graphical representation of the performance of a classifier at various classification thresholds, while the AUC score summarizes its performance over all possible thresholds. By understanding these concepts, we can better evaluate the performance of our classifiers and make more informed decisions.