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Explore Stack InternalAre you perhaps doing ensembles?
Usually, for imbalanced dataset, the easiest way is to oversample or undersample the data. You either repeat some data on classes containing small samples or cut-off some sample data on classes with very high frequency to make a balanced dataset.
Other technique is to use weights for classes that is relevantwith respect to the frequency of each class.
Another one is to build a model that generates artificial inputs like that in generative adversarial networks.
Are you perhaps doing ensembles?
Usually, for imbalanced dataset, the easiest way is to oversample or undersample the data. You either repeat some data on classes containing small samples or cut-off some sample data on classes with very high frequency to make a balanced dataset.
Other technique is to use weights for classes that is relevant to the frequency of each class.
Another one is to build a model that generates artificial inputs like that in generative adversarial networks.
Are you perhaps doing ensembles?
Usually, for imbalanced dataset, the easiest way is to oversample or undersample the data. You either repeat some data on classes containing small samples or cut-off some sample data on classes with very high frequency to make a balanced dataset.
Other technique is to use weights for classes with respect to the frequency of each class.
Another one is to build a model that generates artificial inputs like that in generative adversarial networks.
Are you perhaps doing ensembles?
Usually, for imbalanced dataset, the easiest way is to oversample or undersample the data. You either repeat some data on classes containing small samples or cut-off some sample data on classes with very high frequency to make a balanced dataset.
Other technique is to use weights for classes that is relevant to the frequency of each class.
Another one is to build a model that generates artificial inputs like that in generative adversarial networks.
