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3 cards from this section. Quick, good the night before a test.
What is the formula for calculating accuracy?
Think about how many correct predictions you need compared to all predictions.
Why might accuracy be a misleading metric?
Think about scenarios with many more negative cases.
What is the main formula used to calculate accuracy?
Remember both positive and negative predictions are included.
True or False: Accuracy can be a reliable metric for all types of data sets.
Think about how many predictions each class has.
In a scenario with 300 instances: 200 actual negatives and 100 positives, a model predicts 80 positives but incorrectly identifies 30 as negatives. Calculate TP, TN, FP, FN, and accuracy.
Break down the predictions using the definitions given earlier.
You have a dataset with an extreme imbalance, say 90% negatives and only 10% positives. If your model predicts 95% correctly but misses most positives, discuss the implications of this accuracy.
Consider what accuracy alone fails to explain in performance.
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