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Tweedie Regression Post-Rebase (#1737)

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* add support for tweedie regression

* added back readme line that was accidentally deleted

* fixed linting errors

* add support for tweedie regression

* added back readme line that was accidentally deleted

* fixed linting errors

* rebased with upstream master and added R example

* changed parameter name to tweedie_variance_power

* linting error fix

* refactored tweedie-nloglik metric to be more like the other parameterized metrics

* added upper and lower bound check to tweedie metric

* add support for tweedie regression

* added back readme line that was accidentally deleted

* fixed linting errors

* added upper and lower bound check to tweedie metric

* added back readme line that was accidentally deleted

* rebased with upstream master and added R example

* rebased again on top of upstream master

* linting error fix

* added upper and lower bound check to tweedie metric

* rebased with master

* lint fix

* removed whitespace at end of line 186 - elementwise_metric.cc
This commit is contained in:
Tony DiFranco
2016-11-05 20:02:32 -04:00
committed by Tianqi Chen
parent 52b9867be5
commit 2ad0948444
4 changed files with 156 additions and 0 deletions
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doc/parameter.md
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@@ -107,6 +107,11 @@ Parameters for Linear Booster
* lambda_bias
- L2 regularization term on bias, default 0(no L1 reg on bias because it is not important)
Parameters for Tweedie Regression
-----------------------------
* tweedie_variance_power [default=1.5]
- Parameter that controls the variance of the tweedie distribution. Set closer to 2 to shift towards a gamma distribution and closer to 1 to shift towards a poisson distribution.
Learning Task Parameters
------------------------
Specify the learning task and the corresponding learning objective. The objective options are below:
@@ -121,6 +126,8 @@ Specify the learning task and the corresponding learning objective. The objectiv
- "multi:softprob" --same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata, nclass matrix. The result contains predicted probability of each data point belonging to each class.
- "rank:pairwise" --set XGBoost to do ranking task by minimizing the pairwise loss
- "reg:gamma" --gamma regression for severity data, output mean of gamma distribution
- "reg:tweedie" --tweedie regression for insurance data
- tweedie_variance_power is set to 1.5 by default in tweedie regression and must be in the range [1, 2)
* base_score [ default=0.5 ]
- the initial prediction score of all instances, global bias
- for sufficient number of iterations, changing this value will not have too much effect.