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bagging 包装器似乎给出了奇怪的结果。如果我将其应用于简单的逻辑回归，那么 logloss 会放大 10 倍：

library(mlbench)
library(mlr)

data(PimaIndiansDiabetes)

trainTask1 <- makeClassifTask(data = PimaIndiansDiabetes,target = "diabetes",positive = "pos")

bagged.lrn = makeBaggingWrapper(makeLearner("classif.logreg"), bw.iters = 10, bw.replace = TRUE, bw.size = 0.8, bw.feats = 1)
bagged.lrn = setPredictType(bagged.lrn,"prob")
non.bagged.lrn = setPredictType(makeLearner("classif.logreg"),"prob")

rdesc = makeResampleDesc("CV", iters = 5L)

resample(learner = non.bagged.lrn, task = trainTask1, resampling = rdesc, show.info = FALSE,measures = logloss)
resample(learner = bagged.lrn, task = trainTask1, resampling = rdesc, show.info = FALSE,measures = logloss)

给

Resample Result
Task: PimaIndiansDiabetes
Learner: classif.logreg
logloss.aggr: 0.49
logloss.mean: 0.49
logloss.sd: 0.02
Runtime: 0.0699999

对于第一个学习者和

Resample Result
Task: PimaIndiansDiabetes
Learner: classif.logreg.bagged
logloss.aggr: 5.41
logloss.mean: 5.41
logloss.sd: 0.80

运行时间：0.645

对于袋装的。因此，袋装的性能要差得多。是有错误还是我做错了什么？

这是我的sessionInfo()

R version 3.3.1 (2016-06-21)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252    LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                           LC_TIME=English_United States.1252    

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

other attached packages:
[1] mlr_2.9          stringi_1.1.1    ParamHelpers_1.8 ggplot2_2.1.0    BBmisc_1.10      mlbench_2.1-1   

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.6      magrittr_1.5     splines_3.3.1    munsell_0.4.3    lattice_0.20-33  xtable_1.8-2     colorspace_1.2-6
 [8] R6_2.1.2         plyr_1.8.4       dplyr_0.5.0      tools_3.3.1      parallel_3.3.1   grid_3.3.1       checkmate_1.8.1 
[15] data.table_1.9.6 gtable_0.2.0     DBI_0.4-1        htmltools_0.3.5  ggvis_0.4.3      survival_2.39-4  assertthat_0.1  
[22] digest_0.6.9     tibble_1.1       Matrix_1.2-6     shiny_0.13.2     mime_0.5         parallelMap_1.3  scales_0.4.0    
[29] backports_1.0.3  httpuv_1.3.3     chron_2.3-47
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1 回答 1

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这个结果不一定有什么问题，尽管可以更好地指定 bagging 模型。

Bagging 并不一定总能为您提供更好的性能统计数据，而是帮助您避免过度拟合并提高准确性。

因此，您的 non-bagging 模型具有更好的性能统计数据的原因可能仅仅是它过度拟合或以其他方式产生具有误导性性能统计数据的更偏颇的结果。

然而，这里有一个大大改进的 bagging 模型规范，它使平均 logloss 降低了 70%：

pacman::p_load(mlbench,mlr)

data(PimaIndiansDiabetes)
set.seed(1)

trainTask1 <- makeClassifTask(data = PimaIndiansDiabetes,target = "diabetes",positive = "pos")

bagged.lrn     = makeBaggingWrapper(makeLearner("classif.logreg"), 
                                    bw.iters = 100, 
                                    bw.replace = TRUE, 
                                    bw.size = .6, 
                                    bw.feats = .5)
bagged.lrn     = setPredictType(bagged.lrn,"prob")
non.bagged.lrn = setPredictType(makeLearner("classif.logreg"),"prob")

rdesc = makeResampleDesc("CV", iters = 10L)

resample(learner    = non.bagged.lrn, 
         task       = trainTask1, 
         resampling = rdesc, 
         show.info  = T,
         measures   = logloss)


resample(learner    = bagged.lrn, 
         task       = trainTask1, 
         resampling = rdesc, 
         show.info  = T, 
         measures   = logloss)

关键结果在哪里

Resample Result
Task: PimaIndiansDiabetes
Learner: classif.logreg.bagged
logloss.aggr: 1.65
logloss.mean: 1.65
logloss.sd: 0.90
Runtime: 14.0544
于 2016-09-23T13:57:40.967 回答