问题描述
我正在尝试使用annual_unqiue_count <> 0软件包来拟合SELECT
customerID,sales_date,SUM(annual_unqiue_count),SUM(sales_volume)
FROM sales
GROUP BY 1,2
HAVING SUM(annual_unqiue_count) <> 0;
模型。我知道其他人也遇到了同样的问题，但是这些问题的注释中提供的所有解决方案都无法解决我的错误。这是我的可复制代码：
gbm这是我得到的错误：
caret我已经检查了数据框是否缺少值，没有。有什么问题吗？
library(dplyr)
library(MASS)
library(caret)
library(gbm)
Clean_winter_diff<-structure(list(Total = c(2L,3L,4L,2L,6L,7L,19L,9L,5L,11L,8L,10L,23L,12L,14L,20L,2L),Site = c(1,1,2,1),Night = c(0,0.25,0.666666667,0.5,0.428571429,0.6315789,0.333333333,0.2,0.166666667,0.909090909,0.222222222,0.375,0.75,0.869565217,0.4,0.363636364,0.142857143,0.785714286,0.833333333,0.6,0.285714286,0.857142857,0.8,Day = c(1,0.571428571,0.368421053,0.090909091,0.777777778,0.625,0.130434783,0.636363636,0.214285714,0.714285714,0),distance_forest = c(0.527747223,0.680189568,0.310562619,0.328173668,0.278522078,0.722954456,0.784333633,0.633598813,0.106383899,0.525329032,0.246038608,0.575318257,0.767179738,0.443355317,0.876859332,0.19139315,0.037535778,0.432922864,0.131314978,0.093159023,0.128161967,0.006470757,0.30307544,0.568211372,0.263593171,0.131057648,0.168134106,0.367657292,0.717686941,0.163080941,0.202433621,0.3842,0.165167085,0.929924705,2.120840521,0.484698725,1.078311772,0.366644583,0.340810601,0.298239859,0.195581001,0.02421172,0.464407271,0.198840768,0.054828399,0.489438607,0.295818359,0.110773002,0.496209018,0.67346593,0.214433884,0.108712722,0.529136166,0.639769867,0.396732499,0.483450073,0.001882719,0.248622382,0.925764277,0.175704519,0.622952019,1.142940058,1.133076471,0.224133662,1.083342909,0.745420612,0.377062959,0.08050045,0.162178412,1.361054023,0.123874613,0.49008657,0.638751698,0.167293055,0.306236508,0.581962136,0.269203966,0.01981849,0.389124993,0.333741945,0.089434216,0.172470454,0.174222306,0.298973407,0.139883014,0.455618893,0.612636301,0.372548564,0.35343891,0.583316416,0.291550392,0.530795339,0.07577014,0.844212848,0.106972082,0.992915959,0.044859616,0.820739224,0.799670156,0.316242417,0.319460412,0.810118761,0.500966406,0.377834056,0.940032033,0.151399734,0.28102882,0.212952188,0.073000622,0.370545468,0.872918616,0.104900131,0.081847421,0.216958479,0.008668498,0.007014128,0.495791646,0.02399882,0.297470809,0.490666846,0.415433354,0.301854897,0.365931213,0.692253337,0.165305616,0.640148893,0.835302988,0.768199373,0.153852261,0.134893226,0.540233724,0.335663076,0.102341147,0.195486707,0.362254712,0.324739821,1.697227338,0.520683209,0.020203443,0.275300664,0.259782193,0.051199078,0.217527413,0.550995487,0.656144105,0.277954065,0.091362713,0.769716859,0.817754331,0.531972108,0.330715097,0.795027122,0.818699405,0.113381995,0.73975023,0.342823482,0.760817657,0.817530729,0.700152145,0.88797978,0.29428625,0.108928974,0.074075782,0.747234676,0.543069,0.262442933,0.262835131,0.356383731,0.371421971,0.015478187,0.601986047,0.048889129,0.406113218,0.127855407,0.396601367,0.294174095,1.112770231,0.066093385,0.833489821,0.27603216,0.261494516,0.139170942,0.36716509,0.303017066,0.245362186,0.071559882,0.08333732,0.617973146,0.075376835,0.778806939,0.484474765,0.09264197,0.605744884,0.568592372,0.464302103,0.219293483,0.115301111,0.636074027,0.69132069,0.448515825,0.150593216,0.668861867,0.664099955,0.386919408,0.568691441,0.328245416,0.441309029,0.216574999,0.191497106,0.372996079,0.211736755),Altitude_diff = c(-0.093344147,-0.032953796,-0.166307236,-0.082168137,-0.074024556,0.011625801,-0.035469849,0.023688222,-0.035174545,0.009125112,-0.148026001,-0.136813009,-0.140504929,-0.155278686,-0.141057312,-0.154625722,-0.138962751,0.021278778,-0.112632,-0.121742996,-0.104769694,-0.062242187,-0.105238068,-0.118123369,-0.116926834,-0.057471783,-0.099749664,-0.138632839,-0.086083588,-0.086340958,-0.109178192,-0.09964916,-0.086616302,-0.113422317,-0.145193425,-0.139987988,-0.12330925,-0.062,-0.073519485,-0.0852851,-0.087,-0.041133632,-0.02300371,0.145411285,0.007278729,0.043087274,0.12858374,0.074364258,0.444998927,-0.018522705,-0.028386627,0.007190659,-0.045301581,-0.057804062,0.132843404,0.021017105,-0.078413605,-0.046420864,0.058002304,-0.081611237,0.079912634,-0.050522034,-0.024949936,-0.084849548,-0.062893188,-0.041188028,-0.051312736,-0.01290921,-0.072736145,-0.079543025,-0.016072741,-0.019319687,-0.0213343,0.020119728,-0.071389999,-0.088737882,0.073720496,-0.019645096,-0.059846527,0.08921346,-0.027587019,-0.064136113,-0.06246801,-0.049053955,0.119930542,0.013316631,-0.060812866,-0.010882792,-0.072900299,-0.00263418,0.055887116,-0.057,-0.152,-0.082,-0.134,-0.157,-0.117,-0.128,0.022,-0.129,0.121,0.126,0.091,-0.075,0.014,-0.071,0.009,-0.137,-0.13,-0.131,-0.054,-0.132,-0.093,-0.143,-0.127,-0.089,-0.058,-0.055,-0.15,-0.17,-0.106,-0.177,-0.009,0.008,-0.08,0.067,-0.029,-0.016,0.048,-0.154,-0.133,-0.109,-0.056,0.029,-0.091,0.031,0.032,0.06,0.075,-0.099,0.202,-0.022,0.013,0.118,-0.034,0.224,-0.003,0.095,0.03,0.04,0.105,-0.013,-0.038,-0.043,-0.01,0.046,-0.096,-0.028,-0.033,-0.023,0.066,0.063,-0.041,-0.001,-0.005,-0.025,0.047,-0.002,0.065,-0.019,-0.045,0.0479274,-0.0969804,-0.0511209,-0.1380578,-0.0619915,-0.1375449,0.028642,-0.139097,-0.0267313,-0.0866448,-0.0664405,-0.0098812,0.0950015,-0.0905839,-0.1271573,-0.1345035,0.0696888,0.1161573,-0.001593,-0.0351609,-0.1168084,-0.0487204,-0.0427109,0.0139613,-0.0361378,-0.073785,-0.0521353,0.0207491,-0.0398732,-0.0512241,-0.0480128,-0.0133375,-0.0047241,0.0556789,-0.0389344,-0.0307192,-0.0410356,-0.0436031,-0.0513303,0.0914526,0.108031,0.078924,-0.0482411,-0.0010576,-0.0543727,0.1055158,-0.0347792,0.0091985,-0.0066721),Revisits = c(0,4,5,3,7,9,6,Ratio = c(0,4.75,1.5,2.5,2.2,2.333333333,8,1.428571429,1.333333333,3.285714286,1.714285714,4.5,1.555555556,3.666666667,3.5,1.666666667,3.333333333,1.75,1.25,20,2),Area = c(0.032426,0.035282,0.113383,0.035693,0.041549,0.058353,0.031573,0.057897,0.034298,0.075203,0.038044,0.039534,0.035463,0.056159,0.0319,0.152971,0.063424,0.033137,0.184546,0.054271,0.043699,0.070929,0.086888,0.182135,0.055882,0.063176,0.072119,0.1096,0.035482,0.040162,0.056385,0.042962,0.032754,0.062732,0.056648,0.035606,0.062001,0.117763,0.062311,0.089266,0.078665,0.091633,0.065517,0.037454,0.060411,0.073355,0.035344,0.033497,0.119351,0.044972,0.031568,0.114325,0.068984,0.061986,0.109741,0.033782,0.031849,0.105872,0.055202,0.031857,0.064647,0.031718,0.032588,0.076284,0.036021,0.216575,0.100172,0.06227,0.060081,0.063876,0.224969,0.045917,0.037024,0.077219,0.054039,0.158028,0.067884,0.034719,0.120346,0.044812,0.080923,0.171879,0.069136,0.0417,0.032867,0.11509,0.053077,0.062925,0.033554,0.07492,0.114556,0.096677,0.049153,0.161404,0.073527,0.045258,0.08603,0.091654,0.033591,0.033243,0.060307,0.048489,0.041845,0.031375,0.046293,0.034473,0.044909,0.052535,0.060832,0.082261,0.086662,0.031981,0.053075,0.057269,0.031764,0.039376,0.061771,0.051374,0.081914,0.04886,0.040433,0.056631,0.086457,0.118001,0.033169,0.033734,0.064399,0.065725,0.043722,0.062459,0.032385,0.07605,0.055818,0.067326,0.034017,0.033867,0.052257,0.062541,0.073173,0.069685,0.034166,0.096639,0.081452,0.116512,0.064753,0.12308,0.033466,0.050626,0.068697,0.105123,0.066668,0.075515,0.076373,0.046068,0.032637,0.067892,0.059513,0.032345,0.076412,0.055942,0.057757,0.070641,0.038058,0.04051,0.049283,0.063954,0.040222,0.043146,0.062292,0.05944,0.032226,0.121329,0.086029,0.040616,0.033843,0.037219,0.066294,0.034378,0.117405,0.095093,0.032398,0.062489,0.060033,0.0006219274,0.0004771933187,0.0005009547997,0.0004406716919,0.0005174510498,0.0004356966248,0.0006026420288,0.0004355072708,0.0005670226318,0.0004853354187,0.0005070045624,0.0005619193115,0.0006754835205,0.0004834161072,0.0004468427429,0.000439496521,0.0006436887817,0.0006849831238,0.0005693302002,0.0004349030151,0.0005387456665,0.0004572155151,0.0005252477493,0.0005314183146,0.0005879613037,0.0005381040955,0.0005002150269,0.0005234927775,0.000592482015,0.0005348047689,0.0005223570905,0.0005260328979,0.0005637895386,0.0005767995911,0.000629678894,0.0005354559326,0.0005431971436,0.0005328845113,0.0005311777954,0.0005214696045,0.0006679819946,0.0006827795207,0.0006529239502,0.0005282859904,0.0005745828705,0.0005196272583,0.0006795158081,0.0005336247467,0.0005789768311,0.0005680122375),distance_main = c(1.131059754,0.9597414435,1.256349606,1.078548275,1.855321885,4.111540893,5.445573732,4.717162654,3.192720443,1.230485339,4.582202671,2.234386271,4.464622586,1.793303323,3.049223638,2.517519578,2.538484406,0.2589592261,0.8107408556,1.265087883,2.583951508,0.5704173619,0.150727288,2.880491806,0.4688362577,1.032252927,1.711598417,2.621504704,0.5018857525,0.9121811232,1.467942423,0.5364545556,1.956558175,1.903428792,1.556986206,0.3888441615,0.2643162488,0.06508233719,1.137,1.050285586,1.40077366,3.600281886,2.354502437,1.899786116,3.690234235,2.808763349,0.7511081312,1.271708613,2.662284706,2.675257642,3.518963652,3.64493179,2.047243432,2.681735548,3.55460067,4.471868465,4.870529144,4.073487063,3.088843029,4.176214051,3.878882256,3.798820098,3.638531617,3.78621757,3.517110032,3.885770398,3.298820012,3.207448044,3.236561986,4.13860818,5.461401614,3.068585968,2.839888067,2.545155836,2.390539028,3.996152667,2.813447134,2.336287582,3.609633571,1.994576758,2.756891326,2.963835872,2.077835347,1.981514275,1.698439482,4.559660757,1.832220975,1.538482109,0.4012068882,1.011597874,0.2762621903,0.6604082443,1.726855522,0.4426442882,1.389697061,2.265330127,4.673539548,2.833166846,3.247307991,1.550221184,1.913466888,1.02140226,1.419304966,4.649917894,3.021104929,1.138684662,0.9702250537,0.8674368023,1.363686091,2.237998135,3.078402963,2.612860775,2.659002418,0.7922293863,0.5605036917,2.918464369,2.607222198,2.72011864,3.293449501,0.2339249027,0.09269339846,0.4948047539,0.988393193,3.35986433,3.283307665,0.4664049454,3.579501178,0.9978282525,2.513329669,1.751686648,2.364558742,0.3028119337,0.2667488345,0.5316889235,4.034444068,3.413510363,0.5591667383,3.303219295,1.845610995,2.029920015,1.968676774,1.642599316,2.259782135,1.840349328,2.169684459,1.466603062,1.35662262,1.287059026,1.114386511,0.1013909283,0.5191928737,2.069483497,2.864063592,3.741153421,3.675316052,2.612341652,2.535722998,4.374650663,0.9801658265,4.516729836,4.200885496,3.757806231,2.911160806,0.08124990183,4.160713125,4.82011578,3.805524153,2.356340037,2.528406371,2.849670115,4.335904978,2.334369917,1.682493793,0.9721257977,2.886626751,1.678288529,3.207466146,2.493581595,1.024302173,0.2878921523,1.951664026,0.001168478,1.9688079e-05,0.000181543742,0.000169602217,0.000342252497,3.8581815e-05,0.000831689834,0.000310111829,0.000123848133,0.00027892549,0.000474703505,0.000605096677,0.001312503032,0.000397102961,0.001565818974,0.001649622681,0.0018610356,0.001417062691,0.000275126286,0.000431104276,0.003826022716,8.0019175e-05,0.004124439051,0.004485276435,0.004514712379,0.00294698083,0.001935731554,0.002986659776,0.002716345238,0.002434957234,0.002476156054,0.001893628041,0.001454772675,0.00099942015,0.001028825627,0.001531671726,0.001566268214,0.001890167805,0.000937548652,0.000653203203,0.000456625581,0.001139386805,0.001135244462,0.001190210739,0.000552443287,0.002855486907,0.001430594014,0.000594097595,0.000339933191)),row.names = c(NA,-234L
),class = "data.frame")
mydata = transform(Clean_winter_diff,Site=Site-1)
#separating training and test data
alpha<-0.7
inTrain_diff <- sample(1:nrow(mydata),alpha * nrow(mydata))
train.set.diff <- mydata[inTrain_diff,]
test.set.diff <- mydata[-inTrain_diff,]
winter.boost=gbm(Site~.,data = mydata,n.trees = 10000,shrinkage = 0.01,interaction.depth = 6,cv.folds = 5,verbose = F)
best.iter=gbm.perf(winter.boost,method = "cv")
best.iter
summary(winter.boost)
#Using caret to get model performance in best iteration
set.seed(123)
fitControl = trainControl(method="cv",number=5,returnResamp = "all")
model2 = train(Site~.,data=mydata[complete.cases(mydata),],method="gbm",distribution="bernoulli",trControl=fitControl,verbose=F,tuneGrid=data.frame(.n.trees=best.iter,.shrinkage=0.01,.interaction.depth=1,.n.minobsinnode=1))
：
RMSE Rsquared MAE
Min. : NA Min. : NA Min. : NA
1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
Median : NA Median : NA Median : NA
Mean :NaN Mean :NaN Mean :NaN
3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
Max. : NA Max. : NA Max. : NA
NA's :1 NA's :1 NA's :1
Error: Stopping
In addition: Warning messages:
1: In train.default(x,y,weights = w,...) :
You are trying to do regression and your outcome only has two possible values Are you trying to do classification? If so,use a 2 level factor as your outcome column.
2: In nominalTrainWorkflow(x = x,y = y,wts = weights,info = trainInfo,:
There were missing values in resampled performance measures
解决方法
您正在进行分类，因此需要将因变量设置为train中caret的因数才能起作用：
set.seed(123)
fitControl = trainControl(method="cv",number=5,returnResamp = "all")
mydata$Site = factor(mydata$Site)
model2 = train(Site~.,data=mydata[complete.cases(mydata),],method="gbm",distribution="bernoulli",trControl=fitControl,verbose=F,tuneGrid=data.frame(.n.trees=400,.shrinkage=0.01,.interaction.depth=1,.n.minobsinnode=1))
model2
Stochastic Gradient Boosting
234 samples
9 predictor
2 classes: '0','1'
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 187,187,188,187
Resampling results:
Accuracy Kappa
0.9232192 0.5550649
Tuning parameter 'n.trees' was held constant at a value
Tuning parameter 'n.minobsinnode' was held constant at
a value of 1