對比錯誤，監督分類

我試圖按照this tutorial使用以下數據集：Mushroom Classification。我正在尋找監督分類的問題，我想我明白了。對比錯誤，監督分類

運行下面的代碼後...

library(caret) 

dataset = read.csv("mushrooms.csv") 
dim(dataset) 
sapply(dataset, class) 
head(dataset) 
levels(dataset$class) 

set.seed(100) 
inTrain <- createDataPartition(y=dataset$class,p=.75,list=FALSE) 
str(inTrain) 
training <- dataset[inTrain,] 
testing <- dataset[-inTrain,] 
nrow(training) 
nrow(testing) 

control <- trainControl(method="cv", number=10) 
metric <- "Accuracy" 

train.lda <- train(class ~., data=training, method="lda", trControl=control)

...我看到的數據集有8124行和22個變量-plus的classifier-。

dim(dataset) 
[1] 8124 23

但是執行train我收到以下錯誤時：

Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : 
    contrasts can be applied only to factors with 2 or more levels

尋找各地的網絡，甚至在這裏堆棧溢出，我發現的解釋是，我的預測只有一個因素水平。就像class變量只有一個值？儘管如此，之前在代碼中我檢查了該變量的級別，並且我得到它的級別爲2，因爲它需要兩個值。

levels(dataset$class) 
[1] "e" "p"

因此，我不明白爲什麼我得到錯誤。我的推理有什麼問題？我究竟做錯了什麼？

謝謝。

樣品要求：

structure(list(class = structure(c(2L, 1L, 1L, 2L, 1L, 1L, 1L, 
1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("e", 
"p"), class = "factor"), cap.shape = structure(c(6L, 6L, 1L, 
6L, 6L, 6L, 1L, 1L, 6L, 1L, 6L, 6L, 1L, 6L, 6L, 5L, 3L, 6L, 6L, 
6L, 1L, 6L, 1L, 1L, 1L, 3L, 6L, 6L, 3L, 6L, 1L, 6L, 6L, 6L, 1L, 
6L, 5L, 6L, 6L, 1L, 1L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 3L, 6L, 
6L, 1L, 6L, 6L, 1L, 3L, 1L, 6L, 6L, 5L, 1L, 1L, 1L, 1L, 3L, 6L, 
3L, 6L, 6L, 3L, 1L, 3L, 6L, 1L, 3L, 6L, 3L, 6L, 3L, 6L, 6L, 3L, 
6L, 6L, 6L, 1L, 6L, 3L, 5L, 6L, 1L, 6L, 6L, 6L, 6L, 3L, 6L, 1L, 
6L), .Label = c("b", "c", "f", "k", "s", "x"), class = "factor"), 
    cap.surface = structure(c(3L, 3L, 3L, 4L, 3L, 4L, 3L, 4L, 
    4L, 3L, 4L, 4L, 3L, 4L, 1L, 1L, 1L, 3L, 4L, 3L, 3L, 4L, 4L, 
    4L, 3L, 3L, 4L, 4L, 1L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 4L, 
    1L, 3L, 4L, 4L, 1L, 4L, 3L, 4L, 4L, 3L, 4L, 4L, 4L, 3L, 3L, 
    4L, 3L, 4L, 1L, 3L, 3L, 4L, 1L, 4L, 3L, 4L, 4L, 3L, 3L, 4L, 
    4L, 1L, 1L, 4L, 1L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 3L, 4L, 1L, 
    1L, 4L, 3L, 3L, 3L, 4L, 1L, 1L, 3L, 4L, 4L, 3L, 3L, 4L, 3L, 
    3L, 4L), .Label = c("f", "g", "s", "y"), class = "factor"), 
    cap.color = structure(c(5L, 10L, 9L, 9L, 4L, 10L, 9L, 9L, 
    9L, 10L, 10L, 10L, 10L, 9L, 5L, 4L, 9L, 5L, 9L, 5L, 10L, 
    5L, 10L, 9L, 9L, 9L, 10L, 9L, 5L, 10L, 10L, 9L, 10L, 5L, 
    10L, 10L, 4L, 5L, 10L, 10L, 10L, 10L, 5L, 9L, 10L, 9L, 10L, 
    9L, 10L, 10L, 5L, 9L, 9L, 5L, 9L, 10L, 4L, 9L, 10L, 5L, 4L, 
    10L, 10L, 10L, 9L, 5L, 9L, 10L, 10L, 4L, 10L, 9L, 10L, 5L, 
    10L, 10L, 9L, 5L, 5L, 5L, 5L, 9L, 4L, 4L, 10L, 5L, 9L, 9L, 
    5L, 5L, 5L, 9L, 10L, 10L, 5L, 9L, 5L, 10L, 9L, 9L), .Label = c("b", 
    "c", "e", "g", "n", "p", "r", "u", "w", "y"), class = "factor"), 
    bruises = structure(c(2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 
    2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 
    1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 
    2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 
    2L), .Label = c("f", "t"), class = "factor"), odor = structure(c(7L, 
    1L, 4L, 7L, 6L, 1L, 1L, 4L, 7L, 1L, 4L, 1L, 1L, 7L, 6L, 6L, 
    6L, 7L, 7L, 7L, 1L, 7L, 4L, 1L, 4L, 7L, 1L, 4L, 6L, 1L, 4L, 
    7L, 4L, 4L, 4L, 4L, 6L, 7L, 1L, 4L, 1L, 4L, 6L, 7L, 1L, 1L, 
    4L, 4L, 4L, 4L, 1L, 4L, 4L, 7L, 7L, 1L, 6L, 1L, 4L, 1L, 6L, 
    1L, 4L, 4L, 4L, 6L, 4L, 1L, 1L, 6L, 4L, 4L, 4L, 1L, 1L, 4L, 
    4L, 4L, 7L, 1L, 6L, 7L, 6L, 6L, 4L, 6L, 1L, 4L, 4L, 6L, 6L, 
    4L, 1L, 4L, 6L, 1L, 4L, 1L, 1L, 1L), .Label = c("a", "c", 
    "f", "l", "m", "n", "p", "s", "y"), class = "factor"), gill.attachment = structure(c(2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("a", "f"), class = "factor"), 
    gill.spacing = structure(c(1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 
    2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 
    1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 
    2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 
    1L, 1L), .Label = c("c", "w"), class = "factor"), gill.size = structure(c(2L, 
    1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 
    1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 
    2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 
    2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("b", "n"), class = "factor"), 
    gill.color = structure(c(5L, 5L, 6L, 6L, 5L, 6L, 3L, 6L, 
    8L, 3L, 3L, 6L, 11L, 5L, 6L, 5L, 5L, 6L, 6L, 5L, 5L, 6L, 
    5L, 11L, 3L, 6L, 6L, 11L, 5L, 6L, 3L, 5L, 6L, 8L, 6L, 11L, 
    5L, 11L, 8L, 5L, 6L, 6L, 3L, 8L, 11L, 6L, 5L, 11L, 6L, 11L, 
    11L, 5L, 5L, 5L, 5L, 11L, 6L, 11L, 5L, 8L, 5L, 5L, 3L, 3L, 
    6L, 5L, 6L, 11L, 11L, 8L, 8L, 3L, 11L, 8L, 5L, 8L, 6L, 8L, 
    11L, 6L, 5L, 11L, 6L, 6L, 11L, 5L, 11L, 6L, 11L, 6L, 6L, 
    5L, 3L, 3L, 6L, 3L, 8L, 6L, 3L, 3L), .Label = c("b", "e", 
    "g", "h", "k", "n", "o", "p", "r", "u", "w", "y"), class = "factor"), 
    stalk.shape = structure(c(1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 
    2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 
    1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 
    2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 
    1L, 1L), .Label = c("e", "t"), class = "factor"), stalk.root = structure(c(4L, 
    3L, 3L, 4L, 4L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 3L, 4L, 3L, 3L, 3L, 4L, 3L, 3L, 4L, 2L, 3L, 
    4L, 3L, 5L, 3L, 2L, 4L, 4L, 2L, 3L, 3L, 5L, 4L, 4L, 3L, 3L, 
    3L, 3L, 5L, 5L, 5L, 3L, 3L, 4L, 4L, 3L, 4L, 3L, 3L, 5L, 4L, 
    3L, 3L, 3L, 3L, 4L, 3L, 5L, 3L, 4L, 2L, 3L, 2L, 5L, 3L, 2L, 
    2L, 5L, 4L, 5L, 4L, 4L, 4L, 4L, 5L, 4L, 3L, 3L, 5L, 4L, 4L, 
    3L, 3L, 3L, 4L, 3L, 5L, 3L, 3L, 3L), .Label = c("?", "b", 
    "c", "e", "r"), class = "factor"), stalk.surface.above.ring = structure(c(3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("f", "k", 
    "s", "y"), class = "factor"), stalk.surface.below.ring = structure(c(3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 3L, 
    3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 
    3L, 4L, 3L, 4L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 4L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 3L), .Label = c("f", "k", 
    "s", "y"), class = "factor"), stalk.color.above.ring = structure(c(8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L), .Label = c("b", "c", 
    "e", "g", "n", "o", "p", "w", "y"), class = "factor"), stalk.color.below.ring = structure(c(8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L), .Label = c("b", "c", 
    "e", "g", "n", "o", "p", "w", "y"), class = "factor"), veil.type = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "p", class = "factor"), 
    veil.color = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L), .Label = c("n", "o", "w", "y"), class = "factor"), 
    ring.number = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L), .Label = c("n", "o", "t"), class = "factor"), ring.type = structure(c(5L, 
    5L, 5L, 5L, 1L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 1L, 5L, 
    1L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 1L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 1L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 1L, 5L, 5L, 1L, 5L, 1L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 1L, 5L, 5L, 5L, 5L, 5L), .Label = c("e", "f", 
    "l", "n", "p"), class = "factor"), spore.print.color = structure(c(3L, 
    4L, 4L, 3L, 4L, 3L, 3L, 4L, 3L, 3L, 4L, 3L, 4L, 4L, 3L, 4L, 
    4L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 3L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 3L, 4L, 3L, 3L, 4L, 3L, 4L, 
    3L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 3L, 3L, 4L, 4L, 3L, 3L, 4L, 
    4L, 4L, 4L, 4L, 3L, 3L, 4L, 3L, 4L, 4L, 4L, 4L, 3L, 3L, 4L, 
    7L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 3L, 3L, 3L, 4L, 3L, 4L, 4L, 
    3L, 3L, 3L, 4L, 4L, 4L, 3L, 4L, 3L), .Label = c("b", "h", 
    "k", "n", "o", "r", "u", "w", "y"), class = "factor"), population = structure(c(4L, 
    3L, 3L, 4L, 1L, 3L, 3L, 4L, 5L, 4L, 3L, 4L, 4L, 5L, 1L, 6L, 
    1L, 4L, 4L, 4L, 4L, 5L, 4L, 3L, 4L, 5L, 3L, 3L, 6L, 5L, 3L, 
    4L, 3L, 6L, 4L, 5L, 5L, 4L, 5L, 4L, 4L, 6L, 6L, 5L, 3L, 3L, 
    4L, 3L, 4L, 4L, 4L, 4L, 3L, 5L, 5L, 4L, 1L, 3L, 3L, 6L, 5L, 
    4L, 4L, 3L, 4L, 1L, 4L, 4L, 3L, 5L, 5L, 4L, 5L, 4L, 4L, 5L, 
    5L, 6L, 5L, 6L, 4L, 4L, 6L, 4L, 4L, 4L, 4L, 4L, 6L, 5L, 6L, 
    4L, 4L, 3L, 1L, 4L, 4L, 3L, 4L, 4L), .Label = c("a", "c", 
    "n", "s", "v", "y"), class = "factor"), habitat = structure(c(6L, 
    2L, 4L, 6L, 2L, 2L, 4L, 4L, 2L, 4L, 2L, 4L, 2L, 6L, 2L, 6L, 
    2L, 2L, 6L, 6L, 4L, 2L, 4L, 4L, 4L, 2L, 4L, 4L, 6L, 1L, 4L, 
    6L, 4L, 5L, 4L, 1L, 6L, 6L, 1L, 4L, 2L, 5L, 6L, 2L, 4L, 2L, 
    4L, 4L, 5L, 5L, 2L, 2L, 4L, 6L, 6L, 4L, 2L, 2L, 2L, 5L, 6L, 
    4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 6L, 1L, 4L, 1L, 5L, 2L, 1L, 
    1L, 5L, 6L, 2L, 2L, 2L, 6L, 2L, 2L, 2L, 2L, 2L, 2L, 6L, 6L, 
    2L, 2L, 4L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("d", "g", 
    "l", "m", "p", "u", "w"), class = "factor")), .Names = c("class", 
"cap.shape", "cap.surface", "cap.color", "bruises", "odor", "gill.attachment", 
"gill.spacing", "gill.size", "gill.color", "stalk.shape", "stalk.root", 
"stalk.surface.above.ring", "stalk.surface.below.ring", "stalk.color.above.ring", 
"stalk.color.below.ring", "veil.type", "veil.color", "ring.number", 
"ring.type", "spore.print.color", "population", "habitat"), row.names = c(NA, 
100L), class = "data.frame")

.csv文件的數據的前五行加上頭

class,cap-shape,cap-surface,cap-color,bruises,odor,gill-attachment,gill-spacing,gill-size,gill-color,stalk-shape,stalk-root,stalk-surface-above-ring,stalk-surface-below-ring,stalk-color-above-ring,stalk-color-below-ring,veil-type,veil-color,ring-number,ring-type,spore-print-color,population,habitat 
p,x,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,u 
e,x,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g 
e,b,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m 
p,x,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,u 
e,x,s,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g

來源

2016-12-23 MikelAlejoBR

獲取您的數據集需要一個Kaggle ID才能登錄。您可以使用'dput（head（dataset，100））在這裏發佈示例' – G5W

Ups！我忘了將它包含在帖子中。我剛剛編輯它。謝謝。 – MikelAlejoBR

這可能是你的數據集是不是隨機的，並且什麼我只想說只有你的樣本是真實的，而不是完整的數據集，但許多變量只有一個使用的值。輸入summary(dataset)，你會很快看到一些例子。例如，顯示器的一部分是：

stalk.color.below.ring veil.type veil.color ring.number 
w  :100   p:100  n: 0  n: 0  
b  : 0      o: 0  o:100  
c  : 0      w:100  t: 0  
e  : 0      y: 0     
g  : 0            
n  : 0            
(Other): 0

注意，對於veil.type，只存在一個可能值。

levels(dataset$veil.type) 
[1] "p"

我希望這是您的錯誤消息的來源。

Factors = which(sapply(dataset, class) == "factor") 
sapply(dataset[,Factors], function(x) { length(levels(x)) })

可見veil.type是隻有一個可能的電平的唯一屬性。

來源

2016-12-23 18:39:18 G5W

你說得對。我在整個數據集中檢查了_veil.type_的級別，它返回1.這是否意味着我將無法繼續在教程中繼續學習？我接受你的回答，因爲我認爲你回答了我的問題。謝謝:) – MikelAlejoBR

我對你的教程並不完全確定，但值得嘗試一下'ds2 = dataset [， - 17]'來擺脫這個變量，看看是否能讓你解決這個問題。 – G5W

它的確如此。非常感謝你。 – MikelAlejoBR

對比錯誤，監督分類

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