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Answer: F1 score
In a highly imbalanced dataset where 96% of examples do not contain the logo, the F1 score is the most suitable metric to evaluate model performance. The F1 score provides a balance between precision and recall, making it particularly useful for imbalanced datasets like this one. Precision measures the accuracy of positive predictions, while recall assesses the model's ability to find all positive instances. The F1 score combines both metrics to give a more comprehensive view, ensuring that both false positives and false negatives are considered. Therefore, option D, F1 score, is the correct answer.
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You are developing a binary classification machine learning algorithm to detect whether a scanned document image contains a company's logo. The dataset is highly imbalanced with 96% of the examples not containing the logo. Given this class imbalance, which metric would provide the most balanced and comprehensive evaluation of your model's performance in identifying the presence of the logo?
A
Precision
B
Recall
C
RMSE
D
F1 score
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