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I have an imbalanced data set (96-4 split between No and Yes cases). I am trying to build a decision tree model for classification after balancing my data set(tried different thresholds for oversampling and also a combination of under+over sampling), the model seems to have comparatively higher gains on the test data than on the training data across each percentile of the population. How should I interpret this?

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  • $\begingroup$ What do you mean by gains? What are you measuring exactly? $\endgroup$ – Davide ND Jan 17 at 10:42
  • $\begingroup$ @DavideND i am measuring the percent of churned cases the model captures across each percentile of the population, here is an example of what I mean -> the model captures 19% of churned cases in the 1st 10 percentile of the population on training data but on the test data it captures almost 35% of churned cases in the 1st 10 percentile of the population $\endgroup$ – neha Jan 22 at 15:37

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