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5 cards from this section. Quick, good the night before a test.
What does evaluation in AI aim to assess?
Think about model predictions on new data.
Define accuracy in the context of AI evaluation.
Consider how correct predictions relate to total predictions.
What is the primary goal of evaluation in AI?
Remember the context of model performance.
True or False: Overfitting occurs when a model performs well on unseen data.
Think about how it contrasts with generalization.
A model predicts whether an email is spam or not. After receiving 1,000 emails, it correctly identifies 760 non-spam emails and mistakenly labels 25 legitimate emails as spam (false positives). Calculate the model's precision.
Focus on the formula for precision and the definitions of true positives and false positives.
You are tasked with creating a robust model for detecting fraudulent transactions. How would you design an evaluation strategy ensuring you account for both precision and recall given that fraudulent cases are rare?
Discuss methods to handle imbalanced datasets along with the evaluation metrics.
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