What is the ROC curve in scikit-learn?

What is the ROC curve in scikit-learn?

The 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.

How do I generate a ROC curve using scikit-learn?

You can generate a ROC curve using the roc_curve function from scikit-learn. First, you need to have your true labels and predicted probabilities. Then, you can call the function and plot the results using matplotlib.

Can you show me a code example for that?

Sure! Here's a simple example:

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

# Assuming y_true are your 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 does AUC mean in this context?

AUC stands for Area Under the Curve. It quantifies the overall performance of the classifier. AUC values range from 0 to 1, where 1 indicates a perfect classifier and 0.5 indicates a model with no discrimination ability.