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I am trying to make model with logistic regression. My results are:

At first my results with CUTOFF 0.5 was: good borrowers predicted: 95.02% and 33.05% for bad borrowers.

then I realize that the predicton of bad borrowers are low only (33.05%) so I try to make better results so then I changed the cut off to 0.2 and the results was: good borrowers predicted: 74.97% and 75.91% for bad borrowers.

My question is why is also important to have high score of good borrowers for model? Are these results considered a good?

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Yes it is true that is unbalanced is that not good sample then?

Yes overall model with cutoff at 0.5 does better but only predict 33.05% bad borrowers which is not good.

I do this in Eviews (which I regret) because you can not do ROC curve inside this software.

Why is important to predict good borrowers also not only bad borrowers? enter image description here

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    $\begingroup$ Sorry, but I simply do not understand... What is the question? $\endgroup$
    – Repmat
    Commented Mar 30, 2017 at 12:14
  • $\begingroup$ Why is important to also have high score dep 0 (good borrowers)? If I set cut off to 0.10 I will get the better results for dep 1 (bad borrowers) but will decrease score of good borrowers but my mentor told me that this score should not be below 0.69%. My question is why is this important? $\endgroup$
    – Roga Men
    Commented Mar 31, 2017 at 9:21
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    $\begingroup$ What are the consequences of calling a bad borrower a good one & vice versa? If there aren't any consequences you might as well not bother to specify a cut-off. If there are, you can use their relative desirability to decide an appropriate cut-off. $\endgroup$ Commented Mar 31, 2017 at 10:01
  • $\begingroup$ If the cutoff is 0.5 then model predict only 33% of bad borrowers. If the cutoff is 0.2 then model predict 75.91% of bad borrowers. I would like to make model that will avoid investing money to bad borrowers. So it is clear that you need to adjust cutoff right? And make the % of bad borrowers higher.... But I am wondering why it is also important to have high score of good borrowers. $\endgroup$
    – Roga Men
    Commented Apr 2, 2017 at 7:52

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Your dataset is very unbalanced - there are a lot more good borrowers than bad borrowers in your dataset. In fact, 80.41% of samples are good borrowers.

Therefore, your model could trivially just predict that all borrowers are good and achieve 80.41% accuracy. Your model with cutoff at 0.5 does slightly better than that, with 82.89% accuracy (but what is the confidence interval?).

The cutoff you choose depends on what you are trying to get out of your model. You may want to plot a ROC curve to help you make that decision.

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