Probability distribution matching about rain-part2 HOT 2 OPEN

Nice. I had a similar interest, but for a different reason--I wanted to use
an MSE solver for our MAE problem. I hadn't made near as much progress as
you.

Related, a coworker was suggesting a way of doing something similar but
rank ordering the targets, putting them into a probability/0-1 scale, then
getting that into a normal distribution via qnorm. I haven't tried it yet
either but might do so for that problem and apply it here.

Glad to see the issues being used :-)
On Oct 29, 2015 8:30 PM, "JohnM914" [email protected] wrote:

Could be a wild goose chase here, but maybe you guys have experience that
could take this somewhere...

After creating predictions, I did the following things:

• used R fitdistrplus package to best fit a gamma distribution to the
  data
• rank ordered the predicted values from xgb, keeping integrity with
  ground truth
• rank ordered the best fit gamma distr values and merged into the
  ranked predictions
• computed MAEs and tested blends

Each time the MAE for the fit distribution is close and just a little
higher. Contrary to what I thought, the values are such that blending does
not seem to yield any improvement.
[image: image]
https://cloud.githubusercontent.com/assets/15348323/10837861/86bee74a-7e8c-11e5-9430-ead0c7ce2fe5.png

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#6.

from rain-part2.

I tried ensembling our best model and it didn't go anywhere. The first was a broad blend. The second was just 90-5-5. It could be the XGBoost is that much better. It also could be the method of using the common known output values that does help in an MAE output. Probably a little of both, but something worth paying attention to, perhaps by working through it in validation sets.

from rain-part2.