我有55个观测值的表，其中有5个变量（F，H，R，T，U）和1个分类变量（“组”），其中我有两组。

我正在通过将数据分为训练集（70％）和测试集（30％）进行数据采样。然后，我运行adaboosting并检查其工作原理。 我想获取100个采样的adaboost误差分布。但是分布似乎是离散的，仅输出五个值变量：0、0.0588235294117647、0.117647058823529 0.176470588235294和0.235294117647059.mfinal参数不变。我想应该还有更多！如何运作？

我使用以下代码：

predictions<-list()

for (i in 1:100){

train.ind<-sample(nrow(df),nrow(df) * 0.7)

assign(paste0("ada",i),do.call(boosting,c(formula=Group~F + H + R + T + U,data=substitute(df[train.ind,]),mfinal=50,boos=FALSE,coeflearn='Breiman'),envir = parent.frame()))

assign(paste0("pred",predict(ada,df[-train.ind,]))
predictions[[i]]<-get(paste0("pred",i))$error
                }

hist(100*unlist(predictions),breaks=10,main="Error probability [%] ntrees=10. 100 sampling operations",xlab="AdaBoost error")

dput(df)
structure(list(Group = structure(c(2L,2L,1L,2L
),.Label = c("Canines","Sled"),class = "factor"),F = c(0.263150566678734,0.260347316635598,0.26437277258488,0.265710057607949,0.254866055219663,0.263294264681227,0.261901194801303,0.257318268395066,0.26420207103455,0.252093225560912,0.255473253732324,0.259067858940115,0.259528043446917,0.267331491048901,0.260246447333382,0.26035486437815,0.254553215708594,0.274074579975413,0.262896904742862,0.260504330262876,0.258329960879536,0.262664861154909,0.256148832094211,0.258509128895957,0.256292083925698,0.262358651734143,0.254578103664353,0.255386025800537,0.264120912009577,0.275232714712253,0.265375720277527,0.267601768121804,0.262932226832642,0.263633189245163,0.262826186070212,0.261058637786334,0.262979366135887,0.259232168979912,0.252933156025384,0.263963451214447,0.258511197058683,0.261957295373665,0.253412282699461,0.260748166588172,0.263136039863289,0.255317062006506,0.258822015633545,0.252757763183064,0.260840486010478,0.258620689655172,0.263738813871524,0.26241134751773,0.26405425581719,0.263685152057245,0.262062787572784),H = c(0.242711147002311,0.243850477245014,0.245132979060713,0.241794831140003,0.235370262206577,0.241392449436832,0.236787894677703,0.240434935369935,0.234076675284456,0.236978505926275,0.23489414817613,0.236461115627298,0.241377100655228,0.240778565421122,0.238954656595734,0.237237027626932,0.23562891291975,0.228247507171151,0.235543469567304,0.238348073568565,0.237639956832591,0.237993655975811,0.23053394888479,0.237553985998722,0.238716430501961,0.241044553515742,0.23579805839771,0.244646715997643,0.245211405561299,0.248463204730402,0.237910443860818,0.23772859908127,0.242517289073306,0.230376515634971,0.239386381312522,0.242971498213445,0.248246377553633,0.245227816034538,0.237968589560153,0.235998092571798,0.235639593181493,0.240320284697509,0.239383587641388,0.237939850635807,0.240409493084614,0.239705089012767,0.235291279312896,0.237725562711216,0.251017166425148,0.244410329082034,0.247581475626206,0.244082639531298,0.248022977743474,0.246127343801762,0.246345535241663),R = c(0.23238005068085,0.233913128793082,0.232906768805408,0.234580624702711,0.23729616240706,0.232552468336102,0.23566425708828,0.233370934038501,0.23413197660754,0.241255572873247,0.240609653949119,0.233790113420818,0.239086204963073,0.233644719452121,0.23849468613068,0.236846146329206,0.239755264655663,0.225925420024587,0.239355887920232,0.237429996633718,0.23819641170916,0.232039177131833,0.223832380603256,0.235838907338977,0.236669843303285,0.234916072348618,0.238304558463179,0.235904655883701,0.232124394623714,0.222879222527955,0.233232723139038,0.233871666714818,0.235947441217151,0.242585880964708,0.234693056561268,0.233941777691605,0.229366135886539,0.23539800906269,0.239803390172875,0.236505714593364,0.24647853698133,0.235569395017794,0.242526379716086,0.236207360559779,0.234180854122081,0.240408036487878,0.239601762794737,0.245058343429191,0.234449894103222,0.237875925051173,0.230698942666106,0.233475177304965,0.231384358432554,0.233114688928642,0.230655428424067),T = c(0.261758235638105,0.261889077326307,0.257587479549,0.257914486549337,0.272467520166701,0.262760817545838,0.265646653432713,0.268875862196498,0.267589277073454,0.269672695639567,0.269022944142428,0.270680912011768,0.260008650934782,0.258245224077857,0.262304209940204,0.265561961665713,0.270062606715993,0.271752492828849,0.262203737769602,0.263717599534841,0.265833670578713,0.267302305737446,0.289484838417743,0.268097977766344,0.268321642269056,0.261680722401497,0.271319279474757,0.264062602318119,0.258543287805409,0.253424858029389,0.263481112722616,0.260797966082108,0.258603042876902,0.263404414155158,0.263094376055998,0.262028086308617,0.259408120423941,0.26014200592286,0.269294864241588,0.263532741620391,0.259370672778494,0.262153024911032,0.264677749943065,0.265104622216242,0.262273612930016,0.264569812492848,0.266284942258822,0.264458330676529,0.253692453461153,0.25909305621162,0.257980767836164,0.260030835646007,0.256538408006782,0.25707281521235,0.260936248761486),U = c(0.276642254462421,0.275750907536407,0.274138521440258,0.279385339041277,0.283770344294126,0.273124933319108,0.276770665567999,0.272796198013943,0.273326789343435,0.278824893979485,0.282917535762971,0.269035729493284,0.276381346021371,0.275681845488406,0.280473043309851,0.274957072857482,0.279453614114969,0.265400901516186,0.284438401450319,0.275270067631668,0.277080803992985,0.268341093323935,0.26334299428362,0.27494270078114,0.277070411973316,0.276364671746617,0.277622940087166,0.275489489882784,0.275412200032649,0.267636555236813,0.275475938484053,0.27914367434201,0.281161825726141,0.287341513046201,0.274277898463271,0.272041104617345,0.268317034458041,0.277054269097656,0.276448903327891,0.282483963758864,0.288513266166897,0.280409252669039,0.283610415243301,0.27874587902846,0.274619094771137,0.275604453090517,0.286100299160421,0.288513039597016,0.270078586556683,0.280480764184118,0.274123602187187,0.277940178846747,0.273784368554907,0.282369310276287,0.277372857201026)),na.action = structure(c(`2` = 2L,`4` = 4L,`19` = 18L,`24` = 20L,`28` = 24L,`29` = 25L,`30` = 26L,`32` = 28L,`33` = 29L,`42` = 38L,`54` = 46L,`69` = 54L,`74` = 58L,`77` = 59L,`79` = 60L,`80` = 61L,`83` = 62L),class = "omit"),row.names = c(5L,6L,7L,8L,9L,10L,11L,12L,13L,15L,16L,17L,18L,20L,25L,26L,27L,31L,41L,44L,46L,47L,48L,50L,51L,52L,55L,57L,64L,65L,66L,67L,68L,70L,71L,72L,85L,86L,87L,88L,89L,90L,91L,92L,93L,94L,95L,96L,97L,98L,99L,100L,101L,102L,103L),class = "data.frame")