问题描述
我正在使用插入符号包在 R 中训练模型:
I'm training a model in R with the caret package:
ctrl <- trainControl(method = "repeatedcv", repeats = 3, summaryFunction = twoClassSummary)
logitBoostFit <- train(LoanStatus~., credit, method = "LogitBoost", family=binomial, preProcess=c("center", "scale", "pca"),
trControl = ctrl)
我收到以下警告:
Warning message:
In train.default(x, y, weights = w, ...): The metric "Accuracy" was not in the result set. ROC will be used instead.Warning message:
In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : There were missing values in resampled performance measures.
Something is wrong; all the ROC metric values are missing:
ROC Sens Spec
Min. : NA Min. :0.03496 Min. :0.9747
1st Qu.: NA 1st Qu.:0.03919 1st Qu.:0.9758
Median : NA Median :0.04343 Median :0.9770
Mean :NaN Mean :0.04349 Mean :0.9779
3rd Qu.: NA 3rd Qu.:0.04776 3rd Qu.:0.9795
Max. : NA Max. :0.05210 Max. :0.9821
NA's :3
Error in train.default(x, y, weights = w, ...): Stopping
我安装了 pROC 包:
I installed the pROC package:
install.packages("pROC", repos="http://cran.rstudio.com/")
library(pROC)
Type 'citation("pROC")' for a citation.
Attaching package: ‘pROC’
The following objects are masked from ‘package:stats’:
cov, smooth, var
数据如下:
str(credit)
'data.frame': 8580 obs. of 45 variables:
$ ListingCategory : int 1 7 3 1 1 7 1 1 1 1 ...
$ IncomeRange : int 3 4 6 4 4 3 3 4 3 3 ...
$ StatedMonthlyIncome : num 2583 4326 10500 4167 5667 ...
$ IncomeVerifiable : logi TRUE TRUE TRUE FALSE TRUE TRUE ...
$ DTIwProsperLoan : num 1.8e-01 2.0e-01 1.7e-01 1.0e+06 1.8e-01 4.4e-01 2.2e-01 2.0e-01 2.0e-01 3.1e-01 ...
$ EmploymentStatusDescription: Factor w/ 7 levels "Employed","Full-time",..: 1 4 1 7 1 1 1 1 1 1 ...
$ Occupation : Factor w/ 65 levels "","Accountant/CPA",..: 37 37 20 14 43 58 48 37 37 37 ...
$ MonthsEmployed : int 4 44 159 67 26 16 209 147 24 9 ...
$ BorrowerState : Factor w/ 48 levels "AK","AL","AR",..: 22 32 5 5 14 28 4 10 10 34 ...
$ BorrowerCity : Factor w/ 3089 levels "AARONSBURG","ABERDEEN",..: 1737 3059 2488 654 482 719 895 1699 2747 1903 ...
$ BorrowerMetropolitanArea : Factor w/ 1 level "(Not Implemented)": 1 1 1 1 1 1 1 1 1 1 ...
$ LenderIndicator : int 0 0 0 1 0 0 0 0 1 0 ...
$ GroupIndicator : logi FALSE FALSE FALSE TRUE FALSE FALSE ...
$ GroupName : Factor w/ 83 levels "","00 Used Car Loans",..: 1 1 1 47 1 1 1 1 1 1 ...
$ ChannelCode : int 90000 90000 90000 80000 40000 40000 90000 90000 80000 90000 ...
$ AmountParticipation : int 0 0 0 0 0 0 0 0 0 0 ...
$ MonthlyDebt : int 247 785 1631 817 644 1524 427 817 654 749 ...
$ CurrentDelinquencies : int 0 0 0 0 0 0 0 1 0 1 ...
$ DelinquenciesLast7Years : int 0 10 0 0 0 0 0 0 0 0 ...
$ PublicRecordsLast10Years : int 0 1 0 0 0 0 1 0 1 0 ...
$ PublicRecordsLast12Months : int 0 0 0 0 0 0 0 0 0 0 ...
$ FirstRecordedCreditLine : Factor w/ 4719 levels "1/1/00 0:00",..: 3032 2673 1197 2541 4698 4345 3150 925 4452 2358 ...
$ CreditLinesLast7Years : int 53 30 36 26 7 22 15 20 34 32 ...
$ InquiriesLast6Months : int 2 8 5 0 0 0 0 3 0 0 ...
$ AmountDelinquent : int 0 0 0 0 0 0 0 63 0 15 ...
$ CurrentCreditLines : int 10 10 18 10 4 11 6 10 7 8 ...
$ OpenCreditLines : int 9 10 15 8 3 8 5 7 7 8 ...
$ BankcardUtilization : num 0.26 0.69 0.94 0.69 0.81 0.38 0.55 0.24 0.03 0 ...
$ TotalOpenRevolvingAccounts : int 9 7 12 10 3 5 4 5 4 6 ...
$ InstallmentBalance : int 48648 14827 0 0 0 30916 0 21619 41340 15447 ...
$ RealEstateBalance : int 0 0 577745 0 0 0 191296 0 0 126039 ...
$ RevolvingBalance : int 5265 9967 94966 50511 37871 22463 19550 2436 1223 3236 ...
$ RealEstatePayment : int 0 0 4159 0 0 0 1303 0 0 1279 ...
$ RevolvingAvailablePercent : int 78 52 36 45 18 61 44 74 96 76 ...
$ TotalInquiries : int 8 11 15 2 0 0 1 7 1 1 ...
$ TotalTradeItems : int 53 30 36 26 7 22 15 20 34 32 ...
$ SatisfactoryAccounts : int 52 23 36 26 7 19 15 18 34 29 ...
$ NowDelinquentDerog : int 0 0 0 0 0 0 0 1 0 1 ...
$ WasDelinquentDerog : int 1 7 0 0 0 3 0 1 0 2 ...
$ OldestTradeOpenDate : int 5092001 5011977 12011984 4272000 9081993 9122000 6161987 11181999 9191990 4132000 ...
$ DelinquenciesOver30Days : int 0 6 0 0 0 13 0 2 0 2 ...
$ DelinquenciesOver60Days : int 0 4 0 0 0 0 0 0 0 1 ...
$ DelinquenciesOver90Days : int 0 10 0 0 0 0 0 0 0 0 ...
$ IsHomeowner : logi FALSE FALSE TRUE FALSE FALSE FALSE ...
$ LoanStatus : Factor w/ 2 levels "0","1": 2 1 1 2 2 2 2 2 2 1 .`..
总结(信用)ListingCategory IncomeRangeStatedMonthlyIncome IncomeVerifiable最小.: 0.000 分钟:1.000 分钟:0 模式:逻辑
第 1 行:1.000 第 1 行:3.000 第 1 行:3167 错误:784
中位数:2.000 中位数:4.000 中位数:4750 真:7796
均值:4.997 均值:4.089 均值:5755 NA 值:0
第三区:7.000 第三区:5.000 第三区:7083
最大限度.:20.000 最大:7.000 最大:250000
summary(credit) ListingCategory IncomeRange StatedMonthlyIncome IncomeVerifiable Min. : 0.000 Min. :1.000 Min. : 0 Mode :logical
1st Qu.: 1.000 1st Qu.:3.000 1st Qu.: 3167 FALSE:784
Median : 2.000 Median :4.000 Median : 4750 TRUE :7796
Mean : 4.997 Mean :4.089 Mean : 5755 NA's :0
3rd Qu.: 7.000 3rd Qu.:5.000 3rd Qu.: 7083
Max. :20.000 Max. :7.000 Max. :250000
DTIwProsperLoan EmploymentStatusDescription MonthsEmployed
最小.: 0.0 就业人数 :7182 分钟:-23.00
第一区:0.1 全职:416 第一区:26.00
中位数:0.2 未就业:122 中位数:68.00
平均值:91609.4 其他:475 平均值:97.44
第三课:0.3 兼职:7 第三课:139.00
最大限度.:1000000.0 退休: 32 最大.:755.00
个体经营者:346 北美:5
BorrowerState LenderIndicator GroupIndicator ChannelCode
CA :1056 分钟:0.00000 模式:逻辑最小值.:40000
FL : 608 第 1 行:0.00000 错误:8325 第 1 行:80000
纽约:574 中位数:0.00000 真:255 中位数:80000
TX:532 平均值:0.09196 NA:0 平均值:77196
IL : 443 第三行:0.00000 第三行:90000
遗传算法:最大 343:1.00000 最大:90000
(其他):5024
每月债务当前拖欠拖欠过去7年最小.: 0.0 分钟: 0.0000 分钟:0.000
第 1 条:364.0 第 1 条:0.0000 第 1 条:0.000
中位数:708.0 中位数:0.0000 中位数:0.000
平均值:885.5 平均值:0.4119 平均值:4.009
第三曲:1205.2 第三曲:0.0000 第三曲:3.000
最大限度.:30213.0 最大:21.0000 最大:99.000
DTIwProsperLoan EmploymentStatusDescription MonthsEmployed
Min. : 0.0 Employed :7182 Min. :-23.00
1st Qu.: 0.1 Full-time : 416 1st Qu.: 26.00
Median : 0.2 Not employed : 122 Median : 68.00
Mean : 91609.4 Other : 475 Mean : 97.44
3rd Qu.: 0.3 Part-time : 7 3rd Qu.:139.00
Max. :1000000.0 Retired : 32 Max. :755.00
Self-employed: 346 NA's :5
BorrowerState LenderIndicator GroupIndicator ChannelCode
CA :1056 Min. :0.00000 Mode :logical Min. :40000
FL : 608 1st Qu.:0.00000 FALSE:8325 1st Qu.:80000
NY : 574 Median :0.00000 TRUE :255 Median :80000
TX : 532 Mean :0.09196 NA's :0 Mean :77196
IL : 443 3rd Qu.:0.00000 3rd Qu.:90000
GA : 343 Max. :1.00000 Max. :90000
(Other):5024
MonthlyDebt CurrentDelinquencies DelinquenciesLast7Years Min. : 0.0 Min. : 0.0000 Min. : 0.000
1st Qu.: 364.0 1st Qu.: 0.0000 1st Qu.: 0.000
Median : 708.0 Median : 0.0000 Median : 0.000
Mean : 885.5 Mean : 0.4119 Mean : 4.009
3rd Qu.: 1205.2 3rd Qu.: 0.0000 3rd Qu.: 3.000
Max. :30213.0 Max. :21.0000 Max. :99.000
PublicRecordsLast10Years PublicRecordsLast12Months CreditLinesLast7Years最小.: 0.0000 分钟:0.00000 分钟:2.0
第 1 条:0.0000 第 1 条:0.00000 第 1 条:16.0
中位数:0.0000 中位数:0.00000 中位数:24.0
平均值:0.2809 平均值:0.01364 平均值:26.1
第三曲:0.0000 第三曲:0.00000 第三曲:34.0
最大限度.:11.0000 最大:4.00000 最大:115.0
PublicRecordsLast10Years PublicRecordsLast12Months CreditLinesLast7Years Min. : 0.0000 Min. :0.00000 Min. : 2.0
1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.: 16.0
Median : 0.0000 Median :0.00000 Median : 24.0
Mean : 0.2809 Mean :0.01364 Mean : 26.1
3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.: 34.0
Max. :11.0000 Max. :4.00000 Max. :115.0
InquiriesLast6Months Amount Delinquent CurrentCreditLines OpenCreditLines最小.: 0.0000 分钟: 0 分钟: 0.000 分钟:0.000
第 1 条:0.0000 第 1 条:0 第 1 条:5.000 第 1 条:5.000
中位数:1.0000 中位数:0 中位数:9.000 中位数:8.000
平均值:0.9994 平均值:1195 平均值:9.345 平均值:8.306
第三条:1.0000 第三条:0 第三条:12.000 第三条:11.000
最大限度.:15.0000 最大:179158 最大:54.000 最大:42.000
InquiriesLast6Months AmountDelinquent CurrentCreditLines OpenCreditLines Min. : 0.0000 Min. : 0 Min. : 0.000 Min. : 0.000
1st Qu.: 0.0000 1st Qu.: 0 1st Qu.: 5.000 1st Qu.: 5.000
Median : 1.0000 Median : 0 Median : 9.000 Median : 8.000
Mean : 0.9994 Mean : 1195 Mean : 9.345 Mean : 8.306
3rd Qu.: 1.0000 3rd Qu.: 0 3rd Qu.:12.000 3rd Qu.:11.000
Max. :15.0000 Max. :179158 Max. :54.000 Max. :42.000
银行卡使用总额OpenRevolvingAccounts分期付款余额最小.:0.0000 分钟: 0.000 分钟:0
第 1 行:0.2500 第 1 行:3.000 第 1 行:3338
中位数:0.5400 中位数:6.000 中位数:14453
平均值:0.5182 平均值:6.441 平均值:24900
第三区:0.7900 第三区:9.000 第三区:32238
最大限度.:2.2300 最大:44.000 最大:739371
北美:328
RealEstateBalance RevolvingBalance RealEstatePayment RevolvingAvailablePercent最小.: 0 分钟: 0 分钟: 0.0 分钟:0.00
第 1 行:0 第 1 行:2799 第 1 行:0.0 第 1 行:29.00
中位数:26154 中位数:8784 中位数:346.5 中位数:52.00
平均值:109306 平均值:19555 平均值:830.5 平均值:51.46
第三区:176542 第三区:21110 第三区:1382.2 第三区:75.00
最大限度.:1938421 最大:695648 最大:13651.0 最大:100.00
BankcardUtilization TotalOpenRevolvingAccounts InstallmentBalance Min. :0.0000 Min. : 0.000 Min. : 0
1st Qu.:0.2500 1st Qu.: 3.000 1st Qu.: 3338
Median :0.5400 Median : 6.000 Median : 14453
Mean :0.5182 Mean : 6.441 Mean : 24900
3rd Qu.:0.7900 3rd Qu.: 9.000 3rd Qu.: 32238
Max. :2.2300 Max. :44.000 Max. :739371
NA's :328
RealEstateBalance RevolvingBalance RealEstatePayment RevolvingAvailablePercent Min. : 0 Min. : 0 Min. : 0.0 Min. : 0.00
1st Qu.: 0 1st Qu.: 2799 1st Qu.: 0.0 1st Qu.: 29.00
Median : 26154 Median : 8784 Median : 346.5 Median : 52.00
Mean : 109306 Mean : 19555 Mean : 830.5 Mean : 51.46
3rd Qu.: 176542 3rd Qu.: 21110 3rd Qu.: 1382.2 3rd Qu.: 75.00
Max. :1938421 Max. :695648 Max. :13651.0 Max. :100.00
TotalInquiries TotalTradeItems SatisfactoryAccounts NowDelinquentDerog最小.: 0.00 分钟: 2.0 分钟: 1.00 分钟:0.0000
第 1 条:2.00 第 1 条:16.0 第 1 条:14.00 第 1 条:0.0000
中位数:3.00 中位数:24.0 中位数:21.00 中位数:0.0000
平均值:3.91 平均值:26.1 平均值:23.34 平均值:0.4119
第三曲:5.00 第三曲:34.0 第三曲:30.25 第三曲:0.0000
最大限度.:36.00 最大:115.0 最大:113.00 最大:21.0000
TotalInquiries TotalTradeItems SatisfactoryAccounts NowDelinquentDerog Min. : 0.00 Min. : 2.0 Min. : 1.00 Min. : 0.0000
1st Qu.: 2.00 1st Qu.: 16.0 1st Qu.: 14.00 1st Qu.: 0.0000
Median : 3.00 Median : 24.0 Median : 21.00 Median : 0.0000
Mean : 3.91 Mean : 26.1 Mean : 23.34 Mean : 0.4119
3rd Qu.: 5.00 3rd Qu.: 34.0 3rd Qu.: 30.25 3rd Qu.: 0.0000
Max. :36.00 Max. :115.0 Max. :113.00 Max. :21.0000
WasDelinquentDerog OldestTradeOpenDate DelinquenciesOver30Days最小.: 0.000 分钟: 1011957 分钟:0.000
第 1 条:0.000 第 1 条:4101996 第 1 条:0.000
中位数:1.000 中位数:7191993 中位数:1.000
平均值:2.343 平均值:6934230 平均值:4.332
第三区:3.000 第三区:10011990 第三区:5.000
最大限度.:32.000 最大:12312004 最大:99.000
WasDelinquentDerog OldestTradeOpenDate DelinquenciesOver30Days Min. : 0.000 Min. : 1011957 Min. : 0.000
1st Qu.: 0.000 1st Qu.: 4101996 1st Qu.: 0.000
Median : 1.000 Median : 7191993 Median : 1.000
Mean : 2.343 Mean : 6934230 Mean : 4.332
3rd Qu.: 3.000 3rd Qu.:10011990 3rd Qu.: 5.000
Max. :32.000 Max. :12312004 Max. :99.000
DelinquenciesOver60DaysDelinquenciesOver90Days IsHomeowner LoanStatus最小.: 0.000 分钟:0.000 模式:逻辑 0:1518
第 1 条:0.000 第 1 条:0.000 错误:4264 1:7062
中位数:0.000 中位数:0.000 真:4316
平均值:1.908 平均值:4.009 NA:0
第三曲:2.000 第三曲:3.000
最大限度.:73.000 最大:99.000
DelinquenciesOver60Days DelinquenciesOver90Days IsHomeowner LoanStatus Min. : 0.000 Min. : 0.000 Mode :logical 0:1518
1st Qu.: 0.000 1st Qu.: 0.000 FALSE:4264 1:7062
Median : 0.000 Median : 0.000 TRUE :4316
Mean : 1.908 Mean : 4.009 NA's :0
3rd Qu.: 2.000 3rd Qu.: 3.000
Max. :73.000 Max. :99.000
我没有发现任何缺失值:
I didn't find any missing values:
try(na.fail(credit))
dput(head(credit,4))
structure(list(ListingCategory = c(1L, 7L, 3L, 1L), IncomeRange = c(3L,
4L, 6L, 4L), StatedMonthlyIncome = c(2583.3333, 4326, 10500,
4166.6667), IncomeVerifiable = c(TRUE, TRUE, TRUE, FALSE), DTIwProsperLoan = c(0.18,
0.2, 0.17, 1e+06), EmploymentStatusDescription = structure(c(1L,
4L, 1L, 7L), .Label = c("Employed", "Full-time", "Not employed",
"Other", "Part-time", "Retired", "Self-employed"), class = "factor"),
MonthsEmployed = c(4L, 44L, 159L, 67L), BorrowerState = structure(c(22L,
32L, 5L, 5L), .Label = c("AK", "AL", "AR", "AZ", "CA", "CO",
"CT", "DC", "DE", "FL", "GA", "HI", "ID", "IL", "IN", "KS",
"KY", "LA", "MA", "MD", "MI", "MN", "MO", "MS", "MT", "NC",
"NE", "NH", "NJ", "NM", "NV", "NY", "OH", "OK", "OR", "PA",
"RI", "SC", "SD", "TN", "TX", "UT", "VA", "VT", "WA", "WI",
"WV", "WY"), class = "factor"), LenderIndicator = c(0L, 0L,
0L, 1L), GroupIndicator = c(FALSE, FALSE, FALSE, TRUE), ChannelCode = c(90000L,
90000L, 90000L, 80000L), MonthlyDebt = c(247L, 785L, 1631L,
817L), CurrentDelinquencies = c(0L, 0L, 0L, 0L), DelinquenciesLast7Years = c(0L,
10L, 0L, 0L), PublicRecordsLast10Years = c(0L, 1L, 0L, 0L
), PublicRecordsLast12Months = c(0L, 0L, 0L, 0L), CreditLinesLast7Years = c(53L,
30L, 36L, 26L), InquiriesLast6Months = c(2L, 8L, 5L, 0L),
AmountDelinquent = c(0L, 0L, 0L, 0L), CurrentCreditLines = c(10L,
10L, 18L, 10L), OpenCreditLines = c(9L, 10L, 15L, 8L), BankcardUtilization = c(0.26,
0.69, 0.94, 0.69), TotalOpenRevolvingAccounts = c(9L, 7L,
12L, 10L), InstallmentBalance = c(48648L, 14827L, 0L, 0L),
RealEstateBalance = c(0L, 0L, 577745L, 0L), RevolvingBalance = c(5265L,
9967L, 94966L, 50511L), RealEstatePayment = c(0L, 0L, 4159L,
0L), RevolvingAvailablePercent = c(78L, 52L, 36L, 45L), TotalInquiries = c(8L,
11L, 15L, 2L), TotalTradeItems = c(53L, 30L, 36L, 26L), SatisfactoryAccounts = c(52L,
23L, 36L, 26L), NowDelinquentDerog = c(0L, 0L, 0L, 0L), WasDelinquentDerog = c(1L,
7L, 0L, 0L), OldestTradeOpenDate = c(5092001L, 5011977L,
12011984L, 4272000L), DelinquenciesOver30Days = c(0L, 6L,
0L, 0L), DelinquenciesOver60Days = c(0L, 4L, 0L, 0L), DelinquenciesOver90Days = c(0L,
10L, 0L, 0L), IsHomeowner = c(FALSE, FALSE, TRUE, FALSE),
LoanStatus = structure(c(2L, 1L, 1L, 2L), .Label = c("0",
"1"), class = "factor")), .Names = c("ListingCategory", "IncomeRange",
"StatedMonthlyIncome", "IncomeVerifiable", "DTIwProsperLoan",
"EmploymentStatusDescription", "MonthsEmployed", "BorrowerState",
"LenderIndicator", "GroupIndicator", "ChannelCode", "MonthlyDebt",
"CurrentDelinquencies", "DelinquenciesLast7Years", "PublicRecordsLast10Years",
"PublicRecordsLast12Months", "CreditLinesLast7Years", "InquiriesLast6Months",
"AmountDelinquent", "CurrentCreditLines", "OpenCreditLines",
"BankcardUtilization", "TotalOpenRevolvingAccounts", "InstallmentBalance",
"RealEstateBalance", "RevolvingBalance", "RealEstatePayment",
"RevolvingAvailablePercent", "TotalInquiries", "TotalTradeItems",
"SatisfactoryAccounts", "NowDelinquentDerog", "WasDelinquentDerog",
"OldestTradeOpenDate", "DelinquenciesOver30Days", "DelinquenciesOver60Days",
"DelinquenciesOver90Days", "IsHomeowner", "LoanStatus"), row.names = c(NA,
4L), class = "data.frame")
对出了什么问题有什么想法吗?
Any ideas on what's wrong?
Warning message:
In train.default(x, y, weights = w, ...): The metric "Accuracy" was not in the result set. ROC will be used instead.
# weights: 72 (71 variable)
initial value 5144.538374
iter 10 value 3540.667624
iter 20 value 3329.692768
iter 30 value 3279.191024
iter 40 value 3264.926986
iter 50 value 3259.276647
iter 60 value 3259.056261
final value 3259.032668
converged
# weights: 72 (71 variable)
initial value 5144.538374
iter 10 value 3540.774666
iter 20 value 3330.016829
iter 30 value 3279.545595
iter 40 value 3265.384385
iter 50 value 3259.499032
iter 60 value 3259.353010
final value 3259.342601
converged
# weights: 72 (71 variable)
initial value 5144.538374
iter 10 value 3540.667731
iter 20 value 3329.693092
iter 30 value 3279.191379
iter 40 value 3264.927427
iter 50 value 3259.276899
iter 60 value 3259.056561
final value 3259.032978
converged
# weights: 72 (71 variable)
initial value 5144.538374
iter 10 value 3528.401458
iter 20 value 3314.932958
iter 30 value 3264.117072
iter 40 value 3253.780051
iter 50 value 3253.368959
iter 60 value 3253.359047
final value 3253.358819
converged
# weights: 72 (71 variable)
initial value 5144.538374
iter 10 value 3528.508505
iter 20 value 3315.134599
iter 30 value 3265.021404
iter 40 value 3255.739021
iter 50 value 3253.817833
iter 60 value 3253.697180
final value 3253.671003
converged
# weights: 72 (71 variable)
initial value 5144.538374
iter 10 value 3528.401565
iter 20 value 3314.933160
iter 30 value 3264.117768
iter 40 value 3253.780539
iter 50 value 3253.369030
iter 60 value 3253.359358
final value 3253.359133
converged
# weights: 71 (70 variable)
initial value 5145.231521
iter 10 value 4680.326236
iter 20 value 4672.506024
iter 30 value 3662.998233
iter 40 value 3310.207744
iter 50 value 3252.983656
iter 60 value 3250.400275
iter 70 value 3250.339216
final value 3250.332646
converged
... # 权重:72(71 变量)初始值 5144.538374迭代 10 值 4661.569290迭代 20 值 4652.246624迭代 30 值 3715.472355迭代 40 值 3484.096833迭代 50 值 3254.247424iter 60 值 3248.931841迭代 70 值 3248.154679iter 80 值 3248.129089iter 80 值 3248.129085最终值 3248.128574收敛# 权重:72(71 变量)初始值 5144.538374迭代 10 值 4663.660886迭代 20 值 4654.255466迭代 30 值 3542.473235迭代 40 值 3315.027437迭代 50 值 3250.340679iter 60 值 3248.693378迭代 70 值 3248.455840迭代 80 值 3248.443345iter 80 值 3248.443325iter 80 值 3248.443325最终值 3248.443325收敛# 权重:72(71 变量)初始值 5144.538374迭代 10 值 4661.571382迭代 20 值 4652.248711迭代 30 值 4397.069608迭代 40 值 3532.067046迭代 50 值 3283.179445iter 60 值 3249.518694迭代 70 值 3248.163057iter 80 值 3248.129552最终值 3248.128889收敛警告信息:在nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, 中:重采样的性能指标中存在缺失值.有什么不对；缺少所有 ROC 指标值:ROC 传感器规格
最小.: NA 分钟.:0.01805 分钟:0.9946
第一曲:NA 第一曲:0.01805 第一曲:0.9946
中位数:NA 中位数:0.01805 中位数:0.9946
均值:NaN 均值:0.01805 均值:0.9946
第三曲:NA 第三曲:0.01805 第三曲:0.9946
最大限度.: 不适用:0.01805 最大值:0.9946
不适用:3
train.default(x, y, weights = w, ...) 中的错误:停止
... # weights: 72 (71 variable) initial value 5144.538374 iter 10 value 4661.569290 iter 20 value 4652.246624 iter 30 value 3715.472355 iter 40 value 3484.096833 iter 50 value 3254.247424 iter 60 value 3248.931841 iter 70 value 3248.154679 iter 80 value 3248.129089 iter 80 value 3248.129085 final value 3248.128574 converged # weights: 72 (71 variable) initial value 5144.538374 iter 10 value 4663.660886 iter 20 value 4654.255466 iter 30 value 3542.473235 iter 40 value 3315.027437 iter 50 value 3250.340679 iter 60 value 3248.693378 iter 70 value 3248.455840 iter 80 value 3248.443345 iter 80 value 3248.443325 iter 80 value 3248.443325 final value 3248.443325 converged # weights: 72 (71 variable) initial value 5144.538374 iter 10 value 4661.571382 iter 20 value 4652.248711 iter 30 value 4397.069608 iter 40 value 3532.067046 iter 50 value 3283.179445 iter 60 value 3249.518694 iter 70 value 3248.163057 iter 80 value 3248.129552 final value 3248.128889 converged Warning message: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : There were missing values in resampled performance measures. Something is wrong; all the ROC metric values are missing: ROC Sens Spec
Min. : NA Min. :0.01805 Min. :0.9946
1st Qu.: NA 1st Qu.:0.01805 1st Qu.:0.9946
Median : NA Median :0.01805 Median :0.9946
Mean :NaN Mean :0.01805 Mean :0.9946
3rd Qu.: NA 3rd Qu.:0.01805 3rd Qu.:0.9946
Max. : NA Max. :0.01805 Max. :0.9946
NA's :3
Error in train.default(x, y, weights = w, ...): Stopping
summaryFunction = twoClassSummary 似乎触发了警告.它也发生在这里:
summaryFunction = twoClassSummary appears to trigger the warning.It happens here as well:
ctrl <- trainControl(method = "cv", summaryFunction = twoClassSummary)
multinomSummaryFit <- train(LoanStatus~., credit, method = "multinom", family=binomial,
trControl = ctrl)
Warning message:
In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : There were missing values in resampled performance measures.
Something is wrong; all the ROC metric values are missing:
ROC Sens Spec
Min. : NA Min. :0.01919 Min. :0.9941
1st Qu.: NA 1st Qu.:0.01988 1st Qu.:0.9942
Median : NA Median :0.02056 Median :0.9943
Mean :NaN Mean :0.02011 Mean :0.9943
3rd Qu.: NA 3rd Qu.:0.02056 3rd Qu.:0.9943
Max. : NA Max. :0.02057 Max. :0.9944
NA's :3
Error in train.default(x, y, weights = w, ...): Stopping
推荐答案
尝试将类变量值从0"、1"更改为例如"A" , "B" 然后再试.
Try to change class variable values from "0","1" to e.g. "A" , "B" and try then.
这篇关于有什么不对；缺少所有 ROC 指标值:的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！