How do I evaluate pretrained models for my project?

How do I evaluate pretrained models for my project?

To evaluate pretrained models, you should consider metrics like accuracy, precision, recall, and F1 score based on your specific task.

What metrics are best for evaluating a classification model?

For classification models, accuracy, precision, recall, and the F1 score are commonly used metrics. It depends on your use case.

Can I use cross-validation for evaluating these models?

Yes, cross-validation is a great technique to assess the performance of your models and ensure they generalize well to unseen data.

What are some common pitfalls when evaluating pretrained models?

Common pitfalls include overfitting to the validation set, not considering class imbalance, and failing to use a diverse test set.