使用带有gbm方法的插入符号进行多类分类

Question

我正在解决多类分类问题,并尝试使用广义Boosted模型(R中的gbm包).我遇到的问题:插入符号的train功能method="gbm"似乎不能正确处理多类数据.下面给出一个简单的例子.

library(gbm)
library(caret)
data(iris)
fitControl <- trainControl(method="repeatedcv",
                           number=5,
                           repeats=1,
                           verboseIter=TRUE)
set.seed(825)
gbmFit <- train(Species ~ ., data=iris,
                method="gbm",
                trControl=fitControl,
                verbose=FALSE)
gbmFit

输出是

+ Fold1.Rep1: interaction.depth=1, shrinkage=0.1, n.trees=150 
predictions failed for Fold1.Rep1: interaction.depth=1, shrinkage=0.1, n.trees=150 
- Fold1.Rep1: interaction.depth=1, shrinkage=0.1, n.trees=150 
+ Fold1.Rep1: interaction.depth=2, shrinkage=0.1, n.trees=150 
...
+ Fold5.Rep1: interaction.depth=3, shrinkage=0.1, n.trees=150 
predictions failed for Fold5.Rep1: interaction.depth=3, shrinkage=0.1, n.trees=150 
- Fold5.Rep1: interaction.depth=3, shrinkage=0.1, n.trees=150 
Aggregating results
Selecting tuning parameters
Fitting interaction.depth = numeric(0), n.trees = numeric(0), shrinkage = numeric(0) on full training set
Error in if (interaction.depth < 1) { : argument is of length zero

然而,如果我尝试使用没有插入包装的gbm,我会得到很好的结果.

set.seed(1365)
train <- createDataPartition(iris$Species, p=0.7, list=F)
train.iris <- iris[train,]
valid.iris <- iris[-train,]
gbm.fit.iris <- gbm(Species ~ ., data=train.iris, n.trees=200, verbose=FALSE)
gbm.pred <- predict(gbm.fit.iris, valid.iris, n.trees=200, type="response")
gbm.pred <- as.factor(colnames(gbm.pred)[max.col(gbm.pred)]) ##!
confusionMatrix(gbm.pred, valid.iris$Species)$overall

仅供参考,标记为行的代码##!将返回的类概率矩阵转换predict.gbm为最可能类的因子.输出是

      Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull AccuracyPValue  McnemarPValue 
  9.111111e-01   8.666667e-01   7.877883e-01   9.752470e-01   3.333333e-01   8.467252e-16            NaN 

有关如何使gtm在多类数据上正常工作的任何建议吗？

UPD:

sessionInfo()
R version 2.15.3 (2013-03-01)
Platform: x86_64-pc-linux-gnu (64-bit)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=C                 LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] splines   stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] e1071_1.6-1      class_7.3-5      gbm_2.0-8        survival_2.36-14 caret_5.15-61    reshape2_1.2.2   plyr_1.8        
 [8] lattice_0.20-13  foreach_1.4.0    cluster_1.14.3   compare_0.2-3   

loaded via a namespace (and not attached):
[1] codetools_0.2-8 compiler_2.15.3 grid_2.15.3     iterators_1.0.6 stringr_0.6.2   tools_2.15.3   

Answer 1

这是我正在研究的一个问题.

如果你发布了sessionInfo()的结果会有所帮助.

此外,从https://code.google.com/p/gradientboostedmodels/获取最新的gbm 可能会解决问题.

马克斯