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.

Explain how misclassification penalties are applied in the context of class imbalance.

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What changes are made to the loss function when implementing weighted cross entropy?

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What are the expected outcomes of applying the new cross entropy function in model training?

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