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Scortchi ♦
Scortchi ♦
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What's the measure to assess the binary classification accuracy for imbalanceimbalanced data?

Now I have binary classification problem with positive samples roughly 100 times the number of negative samples. In this case the normal accuracy measure (predict == label) is not a good measure. What other measures there are? Is precision,recall recall for negative sample fine or F-1 measure the best? If the model is a probability model, is AUC  (Area under curve) a good measure?

What's the measure to assess the binary classification accuracy for imbalance data

Now I have binary classification problem with positive samples roughly 100 times the number of negative samples. In this case the normal accuracy measure (predict == label) is not a good measure. What other measures there are? Is precision,recall for negative sample fine or F-1 measure the best? If the model is a probability model, is AUC(Area under curve) a good measure?

What's the measure to assess the binary classification accuracy for imbalanced data?

Now I have binary classification problem with positive samples roughly 100 times the number of negative samples. In this case the normal accuracy measure (predict == label) is not a good measure. What other measures there are? Is precision, recall for negative sample fine or F-1 measure the best? If the model is a probability model, is AUC  (Area under curve) a good measure?

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user83176
user83176
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What's the measure to assess the binary classification accuracy for imbalance data

Now I have binary classification problem with positive samples roughly 100 times the number of negative samples. In this case the normal accuracy measure (predict == label) is not a good measure. What other measures there are? Is precision,recall for negative sample fine or F-1 measure the best? If the model is a probability model, is AUC(Area under curve) a good measure?