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5 cards from this section. Quick, good the night before a test.
What is accuracy in the context of performance metrics?
Think about it as how right the model's guesses are.
What does precision measure?
How many correct positives were predicted?
What does accuracy measure in a machine learning model?
Think about it as how often the model was right.
True or False: F1 Score balances both precision and recall.
Consider if both precision and recall can be represented together.
Your model predicted 150 instances: 80 true positives, 30 false positives, and 40 false negatives. Calculate accuracy, precision, recall, and F1 score, and interpret the results.
Work through the formulas methodically.
Discuss how you would adjust your model if it has high accuracy but low precision. What steps would you take?
Consider what factors affect how the model makes decisions.
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