What are error rate metrics in AI?

What are error rate metrics in AI?

Error rate metrics are measurements used to evaluate the performance of AI models, indicating how often they make incorrect predictions.

Can you give me examples of common error rate metrics?

Sure! Common error rate metrics include accuracy, precision, recall, F1 score, and the confusion matrix.

How do I calculate precision and recall?

Precision is calculated as the number of true positives divided by the sum of true positives and false positives. Recall is the number of true positives divided by the sum of true positives and false negatives.

Why is it important to consider both precision and recall?

Considering both metrics helps to understand the trade-off between false positives and false negatives, which is crucial for model evaluation.