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3 cards from this section. Quick, good the night before a test.
What does TP stand for in a confusion matrix?
Think about what it means when the prediction is correct for the positive class.
What is the purpose of a confusion matrix?
Consider what characteristics are compared.
What does FP stand for in the context of confusion matrix?
Think about what happens when the model makes a mistake in identifying positives.
True or False: In the confusion matrix, true negatives indicate correct positive predictions.
Recall the definitions of true positives and true negatives.
You have a confusion matrix with the following results: TP = 75, TN = 50, FP = 25, FN = 10. Calculate the Precision, Recall, and Accuracy.
Use the definitions for each metric and apply them based on the confusion matrix values.
In a real-world scenario, if your model has many false positives yet high accuracy, what does this indicate about the dataset and the model's performance?
Consider how accuracy alone can be misleading in evaluating model performance.
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