What is an ROC curve and how do I visualize it?

What is an ROC curve and how do I visualize it?

An ROC curve, or Receiver Operating Characteristic curve, is a graphical representation of a classifier's performance. It plots the true positive rate against the false positive rate at various threshold settings. To visualize it, you can use libraries like Matplotlib in Python.

Can you show me a code example for plotting an ROC curve?

Sure! Here's a simple example using Python's scikit-learn and Matplotlib:

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

# Assuming y_true are the true labels and y_scores are the predicted probabilities
fpr, tpr, thresholds = roc_curve(y_true, y_scores)
roc_auc = auc(fpr, tpr)

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')
plt.legend(loc='lower right')
plt.show()

What do the axes of the ROC curve represent?

The x-axis of the ROC curve represents the False Positive Rate (FPR), which is the ratio of negative instances that are incorrectly classified as positive. The y-axis represents the True Positive Rate (TPR), also known as sensitivity or recall, which is the ratio of positive instances that are correctly classified.