I have a dataset with 4700 records and it's a classification problem. Proportion of classes is 33 and 67%

few questions

1) does this proportion qualify dataset as imbalanced ?

2) should I do cross validation and then apply (over/under or SMOTE sampling) or I should first balance my sample through these sampling techniques and then do cross validation?

3) Why is propensity score matching used only in healthcare related studies and not much in other applications?

4) How is Propensity score matching different from other ML classification algorithms?


You should fit preprocessing transformers, i.e. imputation, scalers, encoders, resampling, only to train set and apply them to both train and test respectively. Your dataset is imbalanced and you may expect some improvement using resampling techniques, though you should always confirm it conducting cross validation tests.

  • $\begingroup$ Hi, thanks for the response. Just to make sure I got it right. You are asking me to do sampling (over or under or SMOTE) to the full dataset and achieve a balance in my dataset. Once this is done, do cross validation. Have I understood it right? $\endgroup$ – The Great Dec 9 '19 at 12:08
  • $\begingroup$ upvoted your answer $\endgroup$ – The Great Dec 9 '19 at 12:08
  • $\begingroup$ What do you mean by train and apply them to both train and test in below sentence only to train set and apply them to both train and test respectively. $\endgroup$ – The Great Dec 9 '19 at 12:09
  • $\begingroup$ On the contrary, first you split, fit transformers on train set and then apply them on both train and test. That's basically why you need to use pipeline for imblearn instead of sklearn one. $\endgroup$ – Piotr Rarus Dec 9 '19 at 12:11
  • $\begingroup$ can we break this into simple points. Might be I am struggling to understand due to language proficiency $\endgroup$ – The Great Dec 9 '19 at 12:42

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