What are the main challenges in model evaluation for AI?

What are the main challenges in model evaluation for AI?

Some key challenges include overfitting, data quality, and the choice of evaluation metrics.

Can you explain overfitting and how it affects model evaluation?

Overfitting occurs when a model learns the training data too well, including noise, leading to poor performance on unseen data.

What are some common evaluation metrics used in AI?

Common metrics include accuracy, precision, recall, F1 score, and AUC-ROC, each serving different evaluation needs.

How can I ensure the quality of my evaluation data?

You can ensure quality by using diverse datasets, cleaning data, and validating it against known benchmarks.