I understand I can use various sampling techniques when dealing with imbalanced datasets. However, I wonder how I can build a classification model from the training dataset only including data that are not outliers.

Instead of using sampling techniques, I only want to learn the dataset that does not include outliers when training and do the test with the test dataset that includes normal and outliers.

Hope to hear conceptual and technical advice.

  • $\begingroup$ Why do you want to exclude the outliers from the training data but not the testing data? $\endgroup$
    – Dave
    Dec 1, 2020 at 14:18
  • $\begingroup$ It can be the other way around , too. The thing is the number of outlier data points is extremly small, say I have only 20 outliers and 2,000 normal data point. Anyway, I'd like to hear the way you think, if you don't mind. The thing is that I just wonder if I can a classification model that only learns from the training dataset including only normal data and test with normal and outliers to detect outliers. $\endgroup$
    – oceanus
    Dec 1, 2020 at 14:21


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