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In other words, I am looking to predict students that will fail out of school before it happens. The data includes socioeconomic status and other related variables.

I have tried an XGB binary classification (both tree and forest), but the problem is that it doesn't penalize severely wrong answers (predicting that a student will be in the bottom 3% in terms of grades, but they're actually A+ students). The result is that the average grades of the predicted students is quite low, but the median grades aren't actually that bad - there are a few extremely bad students that pull down the average but not the median.

I have tried a XGB regression (both tree and forest), but the problem is that I can't get the model to focus on the bottom 3%. It seeks to reduce error for all predictions. I couldn't care less about telling the difference between an A student and a B student, I only need to consistently identify the bottom 3%ile.

I was thinking that perhaps this could lend itself to reinforcement learning instead of supervised, but I know nothing about reinforcement... WOuld it be possible to make a reinforcement model where the goal is to minimize the median grades of the 3% of students predicted? Or are there any other machine learning techniques that would work?

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  • $\begingroup$ What kind of data do you have? $\endgroup$ Jun 17, 2020 at 17:08
  • $\begingroup$ 62 features either binary or percentiles. $\endgroup$
    – xxanissrxx
    Jun 17, 2020 at 17:47
  • $\begingroup$ Maybe worth looking at modeling “rare events” approach ? I do think suits your problem here ... $\endgroup$
    – n1tk
    Jun 18, 2020 at 3:05

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Try writing a custom loss function for a regression model!

Keras' neural networks support this, for example. See https://stackoverflow.com/q/43818584/745868

(But many other libraries give support for this as well)

The only special thing about your custom loss function is that it doesn't add up the error of a datapoint if min(pred_y, actual_y) >= THRESHOLD

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  • $\begingroup$ Do you have any links that could help me create a loss function? I have no idea how to... eg. for XGB they have to have gradient and hessian which I don't think is possible for median / min functions? $\endgroup$
    – xxanissrxx
    Jun 17, 2020 at 17:52
  • $\begingroup$ holy crap I just realized how genius this is. I can just take the normal loss function and then exclude any datapoints that aren't in the bottom 3%ile????????? $\endgroup$
    – xxanissrxx
    Jun 17, 2020 at 19:35
  • $\begingroup$ Yes, exactly. Here's an example: heartbeat.fritz.ai/…. Please let me know if you bumped into a wall $\endgroup$ Jun 17, 2020 at 20:00
  • $\begingroup$ I would not exclude everything not at the bottom 3%. You also need to worry about mislabelling an A student as a bottom 3% one ;) $\endgroup$ Jun 17, 2020 at 20:01
  • $\begingroup$ Ahhh ok understood. Is there a way to see the default objective function codes so I can start there and add my threshold? $\endgroup$
    – xxanissrxx
    Jun 17, 2020 at 20:37

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