How can we measure the success of AI training?
There are several metrics to evaluate AI training success, such as accuracy, precision, recall, and F1 score.
What is the difference between precision and recall?
Precision measures the accuracy of positive predictions, while recall measures the ability to find all relevant instances.
Can you give an example of how to calculate these metrics?
Sure! If you have 100 positive cases and your model predicts 80 correctly, precision is 80/100 = 0.8. If it misses 20, recall is 80/100 = 0.8.
What other factors should we consider when evaluating AI?
In addition to metrics, consider factors like model robustness, generalization to new data, and user satisfaction.
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