What are some common evaluation methods for AI models?

What are some common evaluation methods for AI models?

Common evaluation methods include accuracy, precision, recall, F1 score, and ROC-AUC. Would you like to know more about any specific method?

Can you explain what precision and recall mean?

Sure! Precision measures the accuracy of positive predictions, while recall measures the ability to find all relevant instances. They are often used together to evaluate model performance.

How do I calculate the F1 score?

The F1 score is the harmonic mean of precision and recall. You can calculate it using the formula: F1 = 2 * (precision * recall) / (precision + recall).

What is ROC-AUC?

ROC-AUC stands for Receiver Operating Characteristic - Area Under Curve. It measures the model's ability to distinguish between classes, with a value closer to 1 indicating better performance.