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4 cards from this section. Quick, good the night before a test.
Define True Positives.
Think about what happens when the model gets a positive case right.
What does a False Positive indicate?
Consider the mistakes a model could make in predictions.
What does a confusion matrix help to visualize?
Think about what category of models uses confusion matrices.
True or False: True Negatives (TN) indicates correct predictions of negative cases.
Consider how the terms are defined.
You are tasked with improving a classification model for a credit approval system. The confusion matrix shows high false negatives. Discuss strategies to reduce them and enhance the model's recall.
Consider aspects of model training and evaluation.
Given a confusion matrix with TP = 40, FP = 25, FN = 15, and TN = 20, calculate precision, recall, and the F1 Score. Discuss how these metrics could inform model improvement.
Make sure to follow through the calculations accurately.
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