How do we evaluate AI models across various domains?

How do we evaluate AI models across various domains?

Evaluating AI models in different domains involves assessing their performance, generalizability, and adaptability to specific tasks.

What metrics should we use for evaluation?

Common metrics include accuracy, precision, recall, F1 score, and area under the ROC curve, depending on the task.

Are there specific challenges in cross-domain evaluation?

Yes, challenges include data variability, domain shift, and the need for domain-specific knowledge to interpret results.

Can you give examples of models used in different domains?

Sure! For instance, convolutional neural networks are popular in image processing, while recurrent neural networks excel in natural language processing.