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5 cards from this section. Quick, good the night before a test.
Define accuracy in the context of classification tasks.
Think about the correct predictions versus the total predictions.
What does precision measure?
Consider what true positives are.
What is the main purpose of evaluation metrics in NLP?
Think about how we determine whether a model is good or not.
Which metric is NOT commonly used for evaluating classification tasks?
Remember the specific application of each metric.
A model for identifying spam in emails achieved a high accuracy but failed to catch many actual spam emails. Discuss how precision and recall provide a clearer picture of the model's effectiveness.
Consider how the model's predictions relate to the actual spam emails.
Propose how you would evaluate a summarization model's output. What metrics would you choose, and why?
Think about what you want to measure in the output quality.
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