My data has 13,000 rows, 7% belong to the minority class. I used SMOTE (Synthetic Minority Oversampling TEchnique) for class balancing such that I raised the ratio of minority class to 42 % and number of rows became 12,655. Now to fit a logistic regression I need to divide the sample for cross validation and testing. I tried two approaches:
- train my data on sample obtained after SMOTE and tested on the
original sample having 13,000 rows,
- divide the sample obtained after SMOTE into train and test and do the fitting and testing on this data set only.
With the first approach my results might get skewed so which approach should I adopt and why?