How can I visualize model evaluation metrics effectively?

How can I visualize model evaluation metrics effectively?

You can use various visualization techniques like confusion matrices, ROC curves, and precision-recall curves.

What is a confusion matrix and how do I interpret it?

A confusion matrix is a table that shows the performance of a classification model. It displays true positives, false positives, true negatives, and false negatives.

Can you show me how to create a ROC curve?

Sure! You can use libraries like Matplotlib and Scikit-learn in Python to plot the ROC curve by calculating the true positive rate and false positive rate.

What are some common pitfalls in visualizing model evaluation?

Common pitfalls include mislabeling axes, using inappropriate scales, and failing to provide context for the visualizations.