How do I implement a ROC curve in Python?

How do I implement a ROC curve in Python?

To implement a ROC curve in Python, you can use libraries like scikit-learn. Do you have a specific dataset in mind?

Yes, I have a binary classification dataset. How do I start?

First, you'll need to split your dataset into training and testing sets. Then, train your model using the training set.

Got it! How do I calculate the true positive rate and false positive rate?

You can use the roc_curve function from scikit-learn, which takes the true labels and predicted probabilities as inputs.

Can you show me a code example?

Sure! Here's a simple example:

from sklearn.metrics import roc_curve
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)
plt.plot(fpr, tpr)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve')
plt.show()