In a classification problem, what metric would you use to measure the performance of a model when the classes are imbalanced?*1 pointo A) Accuracyo B) Precisiono C) Recallo D) F1-score
Question
In a classification problem, what metric would you use to measure the performance of a model when the classes are imbalanced?*1 pointo A) Accuracyo B) Precisiono C) Recallo D) F1-score
Solution
D) F1-score
When dealing with imbalanced classes, accuracy is not a good metric because it can be misleading. Models can have a high accuracy by simply predicting the majority class in all instances. Precision and recall are better metrics for imbalanced classes, but they only provide a partial view of the model's performance.
The F1-score is the harmonic mean of precision and recall, and it tries to find the balance between these two metrics. It is a better measure than accuracy especially in situations where the data is imbalanced. It ranges from 0 to 1, where 1 indicates the best possible performance and 0 the worst.
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