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I'm trying to do a multiclass classification with h2o in R. I stacked a model with a RF, GBM and deeplearning. The accuracy is ok (~0.81), but the average F1 score is bad because class B has a very high error rate. I understand 1 reason is because class B has very little samples (same for training set). Theoretically, what would be a good way to improve the accuracy/lower the error rate for class B?

A Error Rate: 0.1027 = 467 / 4,547 B Error Rate: 0.6847 = 393 / 574 C Error Rate: 0.2347 = 721 / 3,072

Total Error Rate 0.1930 = 1,581 / 8,193

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Why dont you try using SMOTE or some way to generate synthetic data for the class B to balance the classes. UBL is a R packages which is quite useful.

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