Add Poisson splitting rule #495
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@mnwright It would be really great if you could have a look. Feedback is very warmly welcome. |
tests/testthat/test_quantreg.R
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| @@ -2,7 +2,7 @@ library(ranger) | |||
| context("ranger_quantreg") | |||
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| rf.quant <- ranger(mpg ~ ., mtcars[1:26, ], quantreg = TRUE, | |||
| keep.inbag = TRUE, num.trees = 50) | |||
| keep.inbag = TRUE, num.trees = 100, seed = 0) | |||
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Any reason for the change? Or just a mistake commit?
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Before this change, I got
Error in ranger(mpg ~ ., mtcars[1:26, ], quantreg = TRUE, keep.inbag = TRUE, :
Error: Too few trees for out-of-bag quantile regression.It also depends on the seed. This change solved that on my machine. I should have done a separate commit.
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Looks great, thanks! What I have to do before merge (notes to myself):
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Thank you for the fast review! |
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I refactored the Poisson splitting rule a bit to follow I assume, a similar speedup could be possible for the beta splitting rule? |
Yes, that should be possible. |
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@mnwright I merged master and still think this would be nice to have. |
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Sorry for the long silence. I still think this is useful. Let's try to merge it for the next release. |
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Looks good, I think we are ready to merge? |
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Yes, why not just merge? |
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Merged 🎉 |
What does this PR do?
This PR implements a
splitrule = "poisson"with the additional optionpoisson.tauto deal with pure nodes that havey = 0.References
Solves #433.
Further info
The Poisson splitrule is based on the Poisson deviance, but after some arithmetics that makes the split rule computation faster.
The option
poisson.tautakes action if a terminal node hasy=0. It then estimates the value of that node asalpha * 0 + (1-alpha) * mean(parent node)andalpha = samples(node)*mean(parent node) / (poisson.tau + samples(node)*mean(parent node)). The larger the value ofpoisson.tauthe closer the prediction to the parent node's mean. Rpart does it similar.An alternative would have been (or for the future?) to give an option like "minimum sum of responses per node".