R gbm逻辑回归

Question

我希望使用该GBM软件包进行逻辑回归,但它的回答略微超出0-1范围.我已经尝试了0-1预测(bernoulli和adaboost)的建议分布参数,但这实际上比使用更糟糕gaussian.

GBM_NTREES = 150
GBM_SHRINKAGE = 0.1
GBM_DEPTH = 4
GBM_MINOBS = 50
> GBM_model <- gbm.fit(
+ x = trainDescr 
+ ,y = trainClass 
+ ,distribution = "gaussian"
+ ,n.trees = GBM_NTREES
+ ,shrinkage = GBM_SHRINKAGE
+ ,interaction.depth = GBM_DEPTH
+ ,n.minobsinnode = GBM_MINOBS
+ ,verbose = TRUE)
Iter   TrainDeviance   ValidDeviance   StepSize   Improve
     1        0.0603             nan     0.1000    0.0019
     2        0.0588             nan     0.1000    0.0016
     3        0.0575             nan     0.1000    0.0013
     4        0.0563             nan     0.1000    0.0011
     5        0.0553             nan     0.1000    0.0010
     6        0.0546             nan     0.1000    0.0008
     7        0.0539             nan     0.1000    0.0007
     8        0.0533             nan     0.1000    0.0006
     9        0.0528             nan     0.1000    0.0005
    10        0.0524             nan     0.1000    0.0004
   100        0.0484             nan     0.1000    0.0000
   150        0.0481             nan     0.1000   -0.0000
> prediction <- predict.gbm(object = GBM_model
+ ,newdata = testDescr
+ ,GBM_NTREES)
> hist(prediction)
> range(prediction)
[1] -0.02945224  1.00706700

伯努利:

GBM_model <- gbm.fit(
x = trainDescr 
,y = trainClass 
,distribution = "bernoulli"
,n.trees = GBM_NTREES
,shrinkage = GBM_SHRINKAGE
,interaction.depth = GBM_DEPTH
,n.minobsinnode = GBM_MINOBS
,verbose = TRUE)
prediction <- predict.gbm(object = GBM_model
+ ,newdata = testDescr
+ ,GBM_NTREES)
> hist(prediction)
> range(prediction)
[1] -4.699324  3.043440

并且adaboost:

GBM_model <- gbm.fit(
x = trainDescr 
,y = trainClass 
,distribution = "adaboost"
,n.trees = GBM_NTREES
,shrinkage = GBM_SHRINKAGE
,interaction.depth = GBM_DEPTH
,n.minobsinnode = GBM_MINOBS
,verbose = TRUE)
> prediction <- predict.gbm(object = GBM_model
+ ,newdata = testDescr
+ ,GBM_NTREES)
> hist(prediction)
> range(prediction)
[1] -3.0374228  0.9323279

我做错了什么,我是否需要对数据进行预处理(缩放,居中),或者我是否需要进入并手动设置/覆盖值,例如:

prediction <- ifelse(prediction < 0, 0, prediction)
prediction <- ifelse(prediction > 1, 1, prediction)

Answer 1

来自?predict.gbm:

返回预测向量.默认情况下,预测的范围为f(x).例如,对于伯努利损失,返回值在对数优势标度上,对数标度上的泊松损失,以及在对数危险等级上的coxph.

如果type ="response",则gbm将转换回与结果相同的比例.目前唯一的影响是返回bernoulli的概率和泊松的预期计数.对于其他发行版"响应"和"链接"返回相同.

因此,如果您使用distribution="bernoulli",则需要转换预测值以将它们重新缩放为[0,1] : p <- plogis(predict.gbm(model)). 使用distribution="gaussian"实际上是回归而不是分类,虽然我很惊讶预测不在[0,1]中:我的理解是gbm仍然基于树,所以预测的值不应该去超出训练数据中的值.