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4 cards from this section. Quick, good the night before a test.
What does a Confusion Matrix help you visualize?
Think about how a model’s predictions compare to the actual results.
What does TP stand for in the context of a Confusion Matrix?
It is about correctly predicted positive instances.
What does FP stand for in a confusion matrix?
Remember the mistakes the model makes.
True or False: Recall is the measure of true positive predictions.
Think about the positive cases vs. total actual positives.
You developed a model for detecting fraudulent transactions with 1000 transactions examined. Of these, 100 are fraudulent. Your confusion matrix results are: TP = 80, FP = 10, TN = 870, FN = 40. Calculate Accuracy, Precision, Recall, and F1 Score.
Use the relevant metrics formulas and verify calculations carefully.
Discuss potential biases in a confusion matrix if the positive class (e.g., fraud detection) comprises only 5% of your dataset. How might this affect model evaluation?
Think of how class imbalances affect evaluation metrics.
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