What are the main challenges in model interpretability?

What are the main challenges in model interpretability?

The main challenges include complexity of models, lack of transparency, and difficulty in understanding feature importance.

Can you explain why complexity is a challenge?

Complex models, like deep neural networks, have many parameters, making it hard to trace how inputs affect outputs.

What methods can be used to improve interpretability?

Methods like LIME, SHAP, and using simpler models can help improve interpretability by providing insights into model decisions.

Are there any trade-offs between accuracy and interpretability?

Yes, often more complex models achieve higher accuracy but at the cost of being less interpretable.