What are precision and recall?
An example of an AI chatbot conversation about precision and recall in machine learning.
Can you explain what precision and recall are in machine learning?
Sure! Precision is the ratio of true positive predictions to the total positive predictions, while recall is the ratio of true positives to the total actual positives.
Why are precision and recall important?
They help evaluate the performance of a model, especially in cases where class distribution is imbalanced.
Can you give me an example of how to calculate them?
Absolutely! If a model predicts 10 positives, and 7 are correct, precision is 0.7. If there are 10 actual positives and 7 are predicted correctly, recall is also 0.7.
What is the difference between precision and recall?
Precision focuses on the accuracy of positive predictions, while recall measures the ability to find all actual positives.
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