r - r 中的决策树没有用我的训练数据形成

Question

library(caret)
library(rpart.plot)
car_df <- read.csv("TrainingDataSet.csv", sep = ',', header = TRUE)
str(car_df)

set.seed(3033)
intrain <- createDataPartition(y = car_df$Result, p= 0.7, list = FALSE)
training <- car_df[intrain,]
testing <- car_df[-intrain,]
dim(training)
dim(testing)
anyNA(car_df)
trctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 3)
set.seed(3333)
dtree_fit <- train(Result ~., data = training, method = "rpart",
               parms = list(split = "infromation"),
               trControl=trctrl,
               tuneLength = 10)

我收到此警告:

Warning message: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : There were missing values in resampled performance measures.

我正在尝试使用积极和消极情绪的数量来对电影是否受欢迎或失败进行分类。这是我的数据

  dput(car_df) 

structure(list(MovieName = structure(c(20L, 5L, 31L, 26L, 27L, 
12L, 36L, 29L, 38L, 4L, 6L, 8L, 10L, 15L, 18L, 21L, 24L, 34L, 
35L, 7L, 37L, 25L, 23L, 2L, 11L, 40L, 33L, 28L, 14L, 3L, 17L, 
16L, 32L, 22L, 30L, 1L, 19L, 39L, 9L, 13L), .Label = c("#96Movie", 
"#alphamovie", "#APrivateWar", "#AStarIsBorn", "#BlackPanther", 
"#BohemianRhapsody", "#CCV", "#Creed2", "#CrimesOfGrindelwald", 
"#Deadpool2", "#firstman", "#GameNight", "#GreenBookMovie", "#grinchmovie", 
"#Incredibles2", "#indivisiblemovie", "#InstantFamily", "#JurassicWorld", 
"#KolamaavuKokila", "#Oceans8", "#Overlord", "#PariyerumPerumal", 
"#RalphBreaksTheInternet", "#Rampage", "#Ratchasan", "#ReadyPlayerOne", 
"#RedSparrow", "#RobinHoodMovie", "#Sarkar", "#Seemaraja", "#Skyscraper", 
"#Suspiria", "#TheLastKey", "#TheNun", "#ThugsOfHindostan", "#TombRaider", 
"#VadaChennai", "#Venom", "#Vishwaroopam2", "#WidowsMovie"), class = "factor"), 
    PositivePercent = c(40.10554, 67.65609, 80.46796, 71.34831, 
    45.36082, 68.82591, 46.78068, 63.85787, 47.20497, 32.11753, 
    63.7, 39.2, 82.76553, 88.78613, 72.18274, 72.43187, 31.0089, 
    38.50932, 38.9, 19.9, 84.26854, 29.4382, 58.13953, 86.9281, 
    64.54965, 56, 0, 56.61914, 58.82353, 54.98891, 78.21682, 
    90, 64.3002, 85.8, 51.625, 67.71894, 92.21557, 53.84615, 
    40.12158, 68.08081), NegativePercent = c(11.34565, 21.28966, 
    6.408952, 13.10861, 26.80412, 17.10526, 18.61167, 10.55838, 
    46.48033, 56.231, 9.9, 12.1, 9.018036, 6.473988, 13.90863, 
    16.77149, 63.20475, 42.54658, 40.9, 5.4, 3.907816, 2.022472, 
    10.51567, 3.267974, 15.12702, 15.3, 100, 18.12627, 11.76471, 
    13.41463, 5.775076, 10, 20.08114, 2.1, 5.5, 7.739308, 0, 
    34.61538, 12.86727, 10.70707), Result = structure(c(2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
    1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Flop", "Hit"
    ), class = "factor")), class = "data.frame", row.names = c(NA, 
-40L))

Answer 1

> str(car_df)
'data.frame':   40 obs. of  4 variables:
 $ MovieName      : Factor w/ 40 levels "#96Movie","#alphamovie",..: 20 5 31 26 27 12 36 29 38 4 ...
 $ PositivePercent: num  40.1 67.7 80.5 71.3 45.4 ...
 $ NegativePercent: num  11.35 21.29 6.41 13.11 26.8 ...
 $ Result         : Factor w/ 2 levels "Flop","Hit": 2 2 2 2 2 2 2 2 2 1 ...

> with(car_df, table( Result))
Result
Flop  Hit 
   5   35 

 > dtree_fit
CART 

29 samples
 3 predictor
 2 classes: 'Flop', 'Hit' 

因此，您得到了 5 次失败的结果，其中一个预测变量是具有 40 个不同值的变量。鉴于您的每个案例都是独特的并且您的结果严重不平衡，这似乎并不奇怪。数据的存在并不能保证得出实质性结论的可能性。如果这里有任何错误，那就是拟合器中缺少代码，它会说“真的吗？你认为统计包应该能够解决数据严重缺乏的问题？”

顺便说一句:应该是(但不出所料，没有清除警告):

(split = "information")

如果将交叉验证 bin 的数量更改为允许将触发器分布在各个 bin 之间的数字，那么您可以获得非警告结果。鉴于样本量较小，它是否具有很大的有效性仍然值得怀疑:

> trctrl <- trainControl(method = "repeatedcv", number = 3, repeats = 3)
 set.seed(3333)
 dtree_fit <- train(Result ~., data = training, method = "rpart",
                    parms = list(split = "infromation"),
                    trControl=trctrl,
                    tuneLength = 10)
# no warning on one of my runs