Answer-first summary for fast verification
Answer: F1 score
The F1 score is the correct evaluation metric to use when you need to minimize both false positives and false negatives, especially in the presence of class imbalance. The F1 score is the harmonic mean of precision and recall, providing a balanced measure that takes both into account. Unlike accuracy, which can be misleading with imbalanced datasets, the F1 score ensures that both the minority and majority classes are effectively evaluated.
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You are developing a classification model to support predictions for your company’s various products. The dataset you were given for model development has a significant class imbalance, which means that one class is much more frequent than the others. Given this imbalance, you need to choose an evaluation metric that minimizes both false positives and false negatives to ensure the model's effectiveness. Which evaluation metric should you use to properly train the model?
A
F1 score
B
Recall
C
Accuracy
D
Precision
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