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This question already has an answer here:

As in classification we have imbalanced classes, we use up-sampling or down-sampling and other techniques, what do we do when we have imbalanced data in prediction problems, for example, I have distribution of outputs like 90% value1, 5% value2, 3%value3, 2% value4?

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marked as duplicate by oW_, Sean Owen Feb 13 at 3:14

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In addition to post linked by @Wes:

  • You should to check other metrics than accuracy like precision, recall (especially), F1 score or similar.

  • After a certain threshold (lower than the one provided in your example definitely), it might be worthwile to use anomaly detection and use your lesser present classes as such.

I would advise against downsampling as it's leaving informations about data, and IMO you would usually want to avoid that.

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