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决策树在树决策中保持使用Y变量

2016-02-29 144 views
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我使用C5.0来制作决策树，并且它在树中使用我的类标签。下面是我的数据片段。决策树在树决策中保持使用Y变量

trainX

V1    V2  V3   V4 V5     V6 
1 39   State-gov 77516 Bachelors 13  Never-married 
2 50 Self-emp-not-inc 83311 Bachelors 13 Married-civ-spouse 
3 38   Private 215646 HS-grad 9   Divorced 
4 53   Private 234721  11th 7 Married-civ-spouse 
5 28   Private 338409 Bachelors 13 Married-civ-spouse 
      V7    V8  V9  V10 V11 V12 V13   V14 
1  Adm-clerical Not-in-family White Male 2174 0 40 United-States 
2 Exec-managerial  Husband White Male 0 0 13 United-States 
3 Handlers-cleaners Not-in-family White Male 0 0 40 United-States 
4 Handlers-cleaners  Husband Black Male 0 0 40 United-States 
5  Prof-specialty   Wife Black Female 0 0 40   Cuba

trainY

[1] <=50K <=50K <=50K <=50K <=50K

有我的> 50K数据的情况下为好，这个片段的5只是没有包含任何。

当我做我的树，这是我使用

library(C50) 

trainX = X[1:100,] 
trainY = Y[1:100] 
testX = X[101:150,] 
testY = Y[101:150] 

model = C5.0(trainX, trainY) 
summary(model)

代码和输出我得到的是...

Decision tree: 
<=50K (100/25)

评估训练数据（100例）：

Decision Tree 
    ---------------- 
    Size  Errors 

    1 25(25.0%) << 


    (a) (b) <-classified as 
    ---- ---- 
    75   (a): class <=50K 
    25   (b): class >50K

我在做什么错，它使用分类作为树的一部分？

编辑 - DPUTS下面的头。仍然给我同样的问题，其中使用拆分作为决策树< = 50K或> 50K，这是我的“Y”输出，因此不应该是决策过程的一部分。

trainX

structure(list(V1 = c(39L, 50L, 38L, 53L, 28L, 37L), V2 = structure(c(8L, 
7L, 5L, 5L, 5L, 5L), .Label = c(" ?", " Federal-gov", " Local-gov", 
" Never-worked", " Private", " Self-emp-inc", " Self-emp-not-inc", 
" State-gov", " Without-pay"), class = "factor"), V3 = c(77516L, 
83311L, 215646L, 234721L, 338409L, 284582L), V4 = structure(c(10L, 
10L, 12L, 2L, 10L, 13L), .Label = c(" 10th", " 11th", " 12th", 
" 1st-4th", " 5th-6th", " 7th-8th", " 9th", " Assoc-acdm", " Assoc-voc", 
" Bachelors", " Doctorate", " HS-grad", " Masters", " Preschool", 
" Prof-school", " Some-college"), class = "factor"), V5 = c(13L, 
13L, 9L, 7L, 13L, 14L), V6 = structure(c(5L, 3L, 1L, 3L, 3L, 
3L), .Label = c(" Divorced", " Married-AF-spouse", " Married-civ-spouse", 
" Married-spouse-absent", " Never-married", " Separated", " Widowed" 
), class = "factor"), V7 = structure(c(2L, 5L, 7L, 7L, 11L, 5L 
), .Label = c(" ?", " Adm-clerical", " Armed-Forces", " Craft-repair", 
" Exec-managerial", " Farming-fishing", " Handlers-cleaners", 
" Machine-op-inspct", " Other-service", " Priv-house-serv", " Prof-specialty", 
" Protective-serv", " Sales", " Tech-support", " Transport-moving" 
), class = "factor"), V8 = structure(c(2L, 1L, 2L, 1L, 6L, 6L 
), .Label = c(" Husband", " Not-in-family", " Other-relative", 
" Own-child", " Unmarried", " Wife"), class = "factor"), V9 = structure(c(5L, 
5L, 5L, 3L, 3L, 5L), .Label = c(" Amer-Indian-Eskimo", " Asian-Pac-Islander", 
" Black", " Other", " White"), class = "factor"), V10 = structure(c(2L, 
2L, 2L, 2L, 1L, 1L), .Label = c(" Female", " Male"), class = "factor"), 
    V11 = c(2174L, 0L, 0L, 0L, 0L, 0L), V12 = c(0L, 0L, 0L, 0L, 
    0L, 0L), V13 = c(40L, 13L, 40L, 40L, 40L, 40L), V14 = structure(c(40L, 
    40L, 40L, 40L, 6L, 40L), .Label = c(" ?", " Cambodia", " Canada", 
    " China", " Columbia", " Cuba", " Dominican-Republic", " Ecuador", 
    " El-Salvador", " England", " France", " Germany", " Greece", 
    " Guatemala", " Haiti", " Holand-Netherlands", " Honduras", 
    " Hong", " Hungary", " India", " Iran", " Ireland", " Italy", 
    " Jamaica", " Japan", " Laos", " Mexico", " Nicaragua", " Outlying-US(Guam-USVI-etc)", 
    " Peru", " Philippines", " Poland", " Portugal", " Puerto-Rico", 
    " Scotland", " South", " Taiwan", " Thailand", " Trinadad&Tobago", 
    " United-States", " Vietnam", " Yugoslavia"), class = "factor")), .Names = c("V1", 
"V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", 
"V12", "V13", "V14"), row.names = c(NA, 6L), class = "data.frame")

trainY

structure(c(1L, 1L, 1L, 1L, 1L, 1L), .Label = c(" <=50K", " >50K" 
), class = "factor")

在trainX，trainY，阅读重现此问题的最简单的方法后，也可以

library(C50) 
test = C5.0(x=trainX, y=trainY)

我的实际列车Y：

structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 
1L, 2L, 1L, 2L, 2L, 1L, 1L), .Label = c(" <=50K", " >50K"), class = "factor")

我的实际trainX

structure(list(age = c(39L, 50L, 38L, 53L, 28L, 37L, 49L, 52L, 
31L, 42L, 37L, 30L, 23L, 32L, 40L, 34L, 25L, 32L, 38L, 43L, 40L, 
54L, 35L, 43L, 59L, 56L, 19L, 54L, 39L, 49L, 23L, 20L, 45L, 30L, 
22L, 48L, 21L, 19L, 31L, 48L, 31L, 53L, 24L, 49L, 25L, 57L, 53L, 
44L, 41L, 29L, 25L, 18L, 47L, 50L, 47L, 43L, 46L, 35L, 41L, 30L, 
30L, 32L, 48L, 42L, 29L, 36L, 28L, 53L, 49L, 25L, 19L, 31L, 29L, 
23L, 79L, 27L, 40L, 67L, 18L, 31L, 18L, 52L, 46L, 59L, 44L, 53L, 
49L, 33L, 30L, 43L, 57L, 37L, 28L, 30L, 34L, 29L, 48L, 37L, 48L, 
32L), workClass = structure(c(8L, 7L, 5L, 5L, 5L, 5L, 5L, 7L, 
5L, 5L, 5L, 8L, 5L, 5L, 5L, 5L, 7L, 5L, 5L, 7L, 5L, 5L, 2L, 5L, 
5L, 3L, 5L, 1L, 5L, 5L, 3L, 5L, 5L, 2L, 8L, 5L, 5L, 5L, 5L, 7L, 
5L, 7L, 5L, 5L, 5L, 2L, 5L, 5L, 8L, 5L, 5L, 5L, 5L, 2L, 6L, 5L, 
5L, 5L, 5L, 5L, 5L, 1L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 1L, 5L, 5L, 
7L, 5L, 5L, 5L, 5L, 1L, 5L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 5L, 
5L, 2L, 5L, 5L, 5L, 5L, 3L, 3L, 7L, 5L, 5L, 2L), .Label = c(" ?", 
" Federal-gov", " Local-gov", " Never-worked", " Private", " Self-emp-inc", 
" Self-emp-not-inc", " State-gov", " Without-pay"), class = "factor"), 
    fnlwgt = c(77516L, 83311L, 215646L, 234721L, 338409L, 284582L, 
    160187L, 209642L, 45781L, 159449L, 280464L, 141297L, 122272L, 
    205019L, 121772L, 245487L, 176756L, 186824L, 28887L, 292175L, 
    193524L, 302146L, 76845L, 117037L, 109015L, 216851L, 168294L, 
    180211L, 367260L, 193366L, 190709L, 266015L, 386940L, 59951L, 
    311512L, 242406L, 197200L, 544091L, 84154L, 265477L, 507875L, 
    88506L, 172987L, 94638L, 289980L, 337895L, 144361L, 128354L, 
    101603L, 271466L, 32275L, 226956L, 51835L, 251585L, 109832L, 
    237993L, 216666L, 56352L, 147372L, 188146L, 59496L, 293936L, 
    149640L, 116632L, 105598L, 155537L, 183175L, 169846L, 191681L, 
    200681L, 101509L, 309974L, 162298L, 211678L, 124744L, 213921L, 
    32214L, 212759L, 309634L, 125927L, 446839L, 276515L, 51618L, 
    159937L, 343591L, 346253L, 268234L, 202051L, 54334L, 410867L, 
    249977L, 286730L, 212563L, 117747L, 226296L, 115585L, 191277L, 
    202683L, 171095L, 249409L), education = structure(c(10L, 
    10L, 12L, 2L, 10L, 13L, 7L, 12L, 13L, 10L, 16L, 10L, 10L, 
    8L, 9L, 6L, 12L, 12L, 2L, 13L, 11L, 12L, 7L, 2L, 12L, 10L, 
    12L, 16L, 12L, 12L, 8L, 16L, 10L, 16L, 16L, 2L, 16L, 12L, 
    16L, 8L, 7L, 10L, 10L, 12L, 12L, 10L, 12L, 13L, 9L, 9L, 16L, 
    12L, 15L, 10L, 12L, 16L, 5L, 9L, 12L, 12L, 10L, 6L, 12L, 
    11L, 16L, 12L, 16L, 12L, 16L, 16L, 16L, 10L, 10L, 16L, 16L, 
    12L, 8L, 1L, 2L, 6L, 12L, 10L, 12L, 12L, 12L, 12L, 12L, 13L, 
    7L, 11L, 9L, 16L, 16L, 12L, 10L, 16L, 11L, 16L, 8L, 12L), .Label = c(" 10th", 
    " 11th", " 12th", " 1st-4th", " 5th-6th", " 7th-8th", " 9th", 
    " Assoc-acdm", " Assoc-voc", " Bachelors", " Doctorate", 
    " HS-grad", " Masters", " Preschool", " Prof-school", " Some-college" 
    ), class = "factor"), educationNum = c(13L, 13L, 9L, 7L, 
    13L, 14L, 5L, 9L, 14L, 13L, 10L, 13L, 13L, 12L, 11L, 4L, 
    9L, 9L, 7L, 14L, 16L, 9L, 5L, 7L, 9L, 13L, 9L, 10L, 9L, 9L, 
    12L, 10L, 13L, 10L, 10L, 7L, 10L, 9L, 10L, 12L, 5L, 13L, 
    13L, 9L, 9L, 13L, 9L, 14L, 11L, 11L, 10L, 9L, 15L, 13L, 9L, 
    10L, 3L, 11L, 9L, 9L, 13L, 4L, 9L, 16L, 10L, 9L, 10L, 9L, 
    10L, 10L, 10L, 13L, 13L, 10L, 10L, 9L, 12L, 6L, 7L, 4L, 9L, 
    13L, 9L, 9L, 9L, 9L, 9L, 14L, 5L, 16L, 11L, 10L, 10L, 9L, 
    13L, 10L, 16L, 10L, 12L, 9L), marital = structure(c(5L, 3L, 
    1L, 3L, 3L, 3L, 4L, 3L, 5L, 3L, 3L, 3L, 5L, 5L, 3L, 3L, 5L, 
    5L, 3L, 1L, 3L, 6L, 3L, 3L, 1L, 3L, 5L, 3L, 1L, 3L, 5L, 5L, 
    1L, 3L, 3L, 5L, 5L, 2L, 3L, 3L, 3L, 3L, 3L, 6L, 5L, 3L, 3L, 
    1L, 3L, 5L, 3L, 5L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 
    3L, 3L, 1L, 3L, 1L, 3L, 3L, 5L, 5L, 6L, 3L, 5L, 3L, 5L, 3L, 
    3L, 5L, 3L, 5L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 5L, 5L, 3L, 1L, 
    1L, 3L, 3L, 5L, 3L, 3L, 1L, 5L), .Label = c(" Divorced", 
    " Married-AF-spouse", " Married-civ-spouse", " Married-spouse-absent", 
    " Never-married", " Separated", " Widowed"), class = "factor"), 
    occ = structure(c(2L, 5L, 7L, 7L, 11L, 5L, 9L, 5L, 11L, 5L, 
    5L, 11L, 2L, 13L, 4L, 15L, 6L, 8L, 13L, 5L, 11L, 9L, 6L, 
    15L, 14L, 14L, 4L, 1L, 5L, 4L, 12L, 13L, 5L, 2L, 9L, 8L, 
    8L, 2L, 13L, 11L, 8L, 11L, 14L, 2L, 7L, 11L, 8L, 5L, 4L, 
    11L, 5L, 9L, 11L, 5L, 5L, 14L, 8L, 9L, 2L, 8L, 13L, 1L, 15L, 
    11L, 14L, 4L, 2L, 2L, 5L, 1L, 11L, 13L, 13L, 8L, 11L, 9L, 
    2L, 1L, 9L, 6L, 13L, 9L, 9L, 13L, 4L, 13L, 12L, 11L, 13L, 
    11L, 11L, 4L, 8L, 13L, 12L, 7L, 11L, 13L, 5L, 9L), .Label = c(" ?", 
    " Adm-clerical", " Armed-Forces", " Craft-repair", " Exec-managerial", 
    " Farming-fishing", " Handlers-cleaners", " Machine-op-inspct", 
    " Other-service", " Priv-house-serv", " Prof-specialty", 
    " Protective-serv", " Sales", " Tech-support", " Transport-moving" 
    ), class = "factor"), relationship = structure(c(2L, 1L, 
    2L, 1L, 6L, 6L, 2L, 1L, 2L, 1L, 1L, 1L, 4L, 2L, 1L, 1L, 4L, 
    5L, 1L, 5L, 1L, 5L, 1L, 1L, 5L, 1L, 4L, 1L, 2L, 1L, 2L, 4L, 
    4L, 4L, 1L, 5L, 4L, 6L, 1L, 1L, 1L, 1L, 1L, 5L, 2L, 1L, 1L, 
    5L, 1L, 2L, 6L, 4L, 6L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    1L, 1L, 2L, 1L, 2L, 6L, 1L, 4L, 4L, 4L, 1L, 2L, 3L, 4L, 1L, 
    1L, 4L, 1L, 2L, 1L, 6L, 1L, 2L, 4L, 1L, 1L, 2L, 2L, 1L, 5L, 
    5L, 6L, 1L, 2L, 1L, 1L, 5L, 4L), .Label = c(" Husband", " Not-in-family", 
    " Other-relative", " Own-child", " Unmarried", " Wife"), class = "factor"), 
    race = structure(c(5L, 5L, 5L, 3L, 3L, 5L, 3L, 5L, 5L, 5L, 
    3L, 2L, 5L, 3L, 2L, 1L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 5L, 5L, 
    5L, 5L, 2L, 5L, 5L, 5L, 3L, 5L, 5L, 3L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 3L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 2L, 5L, 5L, 5L, 5L, 5L, 3L 
    ), .Label = c(" Amer-Indian-Eskimo", " Asian-Pac-Islander", 
    " Black", " Other", " White"), class = "factor"), sex = structure(c(2L, 
    2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 
    2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 
    2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L), .Label = c(" Female", 
    " Male"), class = "factor"), capGain = c(2174L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 14084L, 5178L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 5013L, 2407L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 14344L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), capLoss = c(0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 2042L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 1408L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1902L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 1573L, 0L, 0L, 1902L, 0L, 0L, 0L), hours = c(40L, 
    13L, 40L, 40L, 40L, 40L, 16L, 45L, 50L, 40L, 80L, 40L, 30L, 
    50L, 40L, 45L, 35L, 40L, 50L, 45L, 60L, 20L, 40L, 40L, 40L, 
    40L, 40L, 60L, 80L, 40L, 52L, 44L, 40L, 40L, 15L, 40L, 40L, 
    25L, 38L, 40L, 43L, 40L, 50L, 40L, 35L, 40L, 38L, 40L, 40L, 
    43L, 40L, 30L, 60L, 55L, 60L, 40L, 40L, 40L, 48L, 40L, 40L, 
    40L, 40L, 45L, 58L, 40L, 40L, 40L, 50L, 40L, 32L, 40L, 70L, 
    40L, 20L, 40L, 40L, 2L, 22L, 40L, 30L, 40L, 40L, 48L, 40L, 
    35L, 40L, 50L, 40L, 50L, 40L, 40L, 25L, 35L, 40L, 50L, 60L, 
    48L, 40L, 40L), country = structure(c(40L, 40L, 40L, 40L, 
    6L, 40L, 24L, 40L, 40L, 40L, 40L, 20L, 40L, 40L, 1L, 27L, 
    40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 36L, 
    40L, 40L, 40L, 40L, 40L, 40L, 40L, 34L, 40L, 40L, 1L, 40L, 
    40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 1L, 
    17L, 40L, 40L, 40L, 27L, 34L, 40L, 40L, 40L, 1L, 40L, 40L, 
    40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 27L, 
    40L, 40L, 40L, 40L, 40L, 6L, 40L, 40L, 40L, 40L, 40L, 40L, 
    40L, 40L, 40L, 40L, 40L, 1L, 40L, 40L, 40L, 40L, 10L, 40L 
    ), .Label = c(" ?", " Cambodia", " Canada", " China", " Columbia", 
    " Cuba", " Dominican-Republic", " Ecuador", " El-Salvador", 
    " England", " France", " Germany", " Greece", " Guatemala", 
    " Haiti", " Holand-Netherlands", " Honduras", " Hong", " Hungary", 
    " India", " Iran", " Ireland", " Italy", " Jamaica", " Japan", 
    " Laos", " Mexico", " Nicaragua", " Outlying-US(Guam-USVI-etc)", 
    " Peru", " Philippines", " Poland", " Portugal", " Puerto-Rico", 
    " Scotland", " South", " Taiwan", " Thailand", " Trinadad&Tobago", 
    " United-States", " Vietnam", " Yugoslavia"), class = "factor")), .Names = c("age", 
"workClass", "fnlwgt", "education", "educationNum", "marital", 
"occ", "relationship", "race", "sex", "capGain", "capLoss", "hours", 
"country"), row.names = c(NA, 100L), class = "data.frame")

来源

2016-02-29 Jibril

+1

你可以确保你的例子是[reproducible]（http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example）。将您的数据包含在更适合于copt/pasteing的表单中。如果我们可以运行代码，它会更容易帮助你。 – MrFlick

+0

有没有可以上传数据的地方？为此格式化它的最佳方式是什么？ @MrFlick – Jibril

+0

请阅读我提供的用于共享最小，可重现的示例的链接。 – MrFlick

回答

1

你用1级（<=50k），因为第一向量输入只包含1L s提供构建一个因子的代码。您应该相应地分配这些标签，或使用更简单的方式来构建响应变量 - 例如trainY <- as.factor(...)。
我改变了trainY被构造为：

y <- structure(c(1L, 2L, 1L, 1L, 2L, 1L), .Label = c(" <=50K", " >50K"), class = "factor")

后重新训练与我有相同的命令树：

Decision tree: 

V14 = Cuba: >50K (1) 
V14 in {?,Cambodia,Canada,China,Columbia,Dominican-Republic,Ecuador, 
    El-Salvador,England,France,Germany,Greece,Guatemala,Haiti, 
    Holand-Netherlands,Honduras,Hong,Hungary,India,Iran,Ireland,Italy, 
    Jamaica,Japan,Laos,Mexico,Nicaragua,Outlying-US(Guam-USVI-etc),Peru, 
    Philippines,Poland,Portugal,Puerto-Rico,Scotland,South,Taiwan,Thailand, 
    Trinadad&Tobago,United-States,Vietnam,Yugoslavia}: <=50K (5/1)

确保你没有在只有一个类响应时通过参数C5.0。心连心

UPDATE
绘制一些预测的VS响应后，我注意到education和educationNum显示最清晰的划分数据（Doctorate意味着立即>50K）。下一步是调整一些非常有用的C5.0 Control选项 - 它们在C5.0package documentation和官方非正式tutorial page中有详细记录 - 检查它们是否允许您对分类控制进行广泛控制。
例如：

C5.0(x = trainX,y = trainY,control = C5.0Control(subset = T, winnow = T,minCases = 4,fuzzyThreshold = T)) 

Decision tree: 

educationNum <= 13 (14.5): <=50K (95/20) 
educationNum >= 16 (14.5): >50K (5)

similiarly，做一些“特色工程”在这种情况下意味着刚刚从生产的原数据帧留出一些列：

C5.0(x = trainX[ ,c(1:5, 9:13)], y = trainY) 

Decision tree: 

educationNum <= 14: <=50K (95/20) 
educationNum > 14: >50K (5)

我相信，有没有人一般“开箱”C5.0默认设置，将产生满意的结果，各种问题，所以它真的归结为尝试不同的参数设置，功能等......但与所有东西R有很多材料周围给你一些方向。

来源

2016-02-29 09:50:54 davidski

+0

你好，先生！ 感谢您的帮助！我遇到的问题稍微复杂一些。在我的例子中，我没有意识到我将遇到的问题都是<= 50K。 在我正在运行的代码中，我使用了100个示例（不希望将它们全部粘贴在这里），并且有一个<=50K and > 50K的组合。如果我使用更大的样本量（150），问题就会消失，但我认为这不重要。你的这个答案是有帮助的，但是如果可以的话，我已经发布了上面使用的更大的Y集。如果你看到任何问题，请让我知道？ @davidski – Jibril

+0

澄清 - 使用1L和2L混合的100个示例，仍然有1级分类器。没有意义。使用150个示例时离开。但要使用100。为什么这是一个问题？新的dput（）在OP中编辑。谢谢！ – Jibril

+0

@Jibril - 请查看更新的答案 – davidski

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