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4 cards from this section. Quick, good the night before a test.
What is accuracy in the context of evaluating an AI model?
Consider how many predictions were right out of total predictions.
What does precision measure?
Think about how true positives relate to positive predictions.
What is the main purpose of the Evaluation phase in AI?
Think about why we check the results of our work.
True or False: A model can be considered good if it has high accuracy but poor precision.
Reflect on how precision relates to correctness.
You have a classification model with the following confusion matrix: 50 true positive, 10 false positive, 5 true negative, and 35 false negative. Calculate the precision, recall, and accuracy of the model.
Use the definitions of precision, recall, and accuracy to perform calculations.
Design a plan to improve an AI model that is performing poorly with an 80% accuracy but has a confusion matrix showing many false negatives. What steps would you take?
Think about which factors contribute to model performance and how to adjust them for improvement.
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