How can I visualize ROC and PR curves for my model?

How can I visualize ROC and PR curves for my model?

You can use libraries like Matplotlib or Seaborn in Python to visualize ROC and PR curves. Do you have a specific dataset in mind?

Yes, I have a binary classification dataset. What steps should I follow?

First, you'll need to calculate the true positive rate and false positive rate for the ROC curve. For the PR curve, calculate precision and recall. Would you like code examples?

Yes, that would be great! Can you provide a sample code?

Sure! Here's a simple example using Scikit-learn:

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

# Assuming y_true and y_scores are your true labels and predicted scores
fpr, tpr, _ = roc_curve(y_true, y_scores)
precision, recall, _ = precision_recall_curve(y_true, y_scores)

plt.figure()
plt.plot(fpr, tpr, label='ROC curve')
plt.plot(recall, precision, label='PR curve')
plt.xlabel('False Positive Rate / Recall')
plt.ylabel('True Positive Rate / Precision')
plt.title('ROC and PR Curves')
plt.legend()
plt.show()

Thanks! How do I interpret these curves?

The ROC curve shows the trade-off between sensitivity and specificity, while the PR curve focuses on the trade-off between precision and recall. A curve closer to the top-left corner indicates better performance.