Evaluate the impact of an AI application used in the real world. (case study) : Working with X-Ray images: Case Study -

The video tutorial discusses the limitations of using accuracy as a metric and introduces AUC as a better alternative. It highlights the problem of class imbalance affecting AUC and proposes a solution using weighted cross entropy. The tutorial explains how to implement this solution by penalizing misclassifications of low-prevalence classes more heavily, thereby improving model performance. The video concludes with a plan to test the new approach in a subsequent lecture.

Why is AUC preferred over accuracy in some cases?

• AUC is always higher than accuracy.

• AUC provides a better measure of model performance when dealing with imbalanced classes.

• AUC is less sensitive to data changes.

• AUC is easier to calculate.

What does an AUC value below 0.5 indicate?

• The model has high accuracy.

• The model is performing perfectly.

• The model is confused between classes.

• The model is overfitting.

What is a common issue that affects AUC and model performance?

• Overfitting

• Class imbalance

• High learning rate

• Low batch size

How does weighted cross entropy help in dealing with class imbalance?

• By using more data for training.

• By penalizing misclassifications of low prevalence classes more heavily.

• By increasing the batch size.

• By reducing the learning rate.

What is the purpose of penalizing misclassifications in weighted cross entropy?

• To ensure the model learns better from errors in low prevalence classes.

• To increase the model's accuracy.

• To decrease the model's complexity.

• To make the model faster.

What is the role of the custom cross entropy function discussed in the lecture?

• To simplify the model architecture.

• To add weighted components to the loss calculation.

• To increase the model's speed.

• To reduce the model's size.

What is the expected outcome of using the custom cross entropy function?

• Improved AUC and better handling of class imbalance.

• Reduced training time.

• Increased model complexity.

• Higher accuracy on balanced datasets.