R '；{：任务1失败-“下标超出范围”x27；_R_Machine Learning_Classification

R '；{：任务1失败-“下标超出范围”x27；

r machine-learning

R '；{：任务1失败-“下标超出范围”x27；,r,machine-learning,classification,R,Machine Learning,Classification,我是R新手，我正在尝试运行此代码，但我总是遇到以下错误： {中出错：任务1失败-“下标超出范围” 这就是我正在运行的代码 svmFit <- train(class ~., method = "svmLinear", data = teacher3, tuneLength = 7, trControl = trainControl( method = "cv", indexOut = teacher3.trai

我是R新手，我正在尝试运行此代码，但我总是遇到以下错误：

{中出错：任务1失败-“下标超出范围”

这就是我正在运行的代码

svmFit <- train(class ~., method = "svmLinear", data = teacher3,
            tuneLength = 7,
            trControl = trainControl(
                method = "cv", indexOut = teacher3.train))

请使用

dput（教师3）

（或代表子集）的结果进行编辑而不是图片。这可能会有帮助：脚本在没有indexout选项的情况下工作。因为我们没有teacher3.train，所以很难说出错误所在。请检查teacher3.train的长度是否与teacher3中的记录数相同。同时确保您有最新版本的插入符号。

C45Fit <- train(class ~ ., method = "J48", data = teacher3,
tuneLength = 5,
trControl = trainControl(
    method = "cv", indexOut = teacher3.train))

ctreeFit <- train(class ~ ., method = "ctree", data = teacher3,
tuneLength = 5,
trControl = trainControl(
    method = "cv", indexOut = teacher3.train))

structure(list(native_speaker = structure(c(1L, 2L, 1L, 1L, 2L, 
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 
2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 
2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
2L, 2L), .Label = c("english speaker", "non-english speaker"), class = "factor"), 
    course_instructor = structure(c(12L, 19L, 12L, 5L, 14L, 12L, 
    20L, 22L, 13L, 19L, 22L, 6L, 10L, 9L, 9L, 9L, 14L, 13L, 6L, 
    14L, 4L, 4L, 3L, 15L, 19L, 14L, 2L, 1L, 18L, 13L, 23L, 10L, 
    6L, 6L, 5L, 17L, 25L, 5L, 1L, 12L, 19L, 12L, 5L, 14L, 12L, 
    20L, 22L, 13L, 19L, 22L, 6L, 10L, 9L, 9L, 9L, 14L, 13L, 6L, 
    14L, 4L, 4L, 3L, 15L, 19L, 14L, 2L, 1L, 18L, 13L, 23L, 10L, 
    6L, 6L, 5L, 17L, 25L, 5L, 1L, 12L, 6L, 17L, 20L, 6L, 10L, 
    13L, 14L, 12L, 12L, 12L, 1L, 21L, 20L, 10L, 21L, 15L, 15L, 
    23L, 13L, 20L, 6L, 9L, 12L, 12L, 9L, 13L, 8L, 12L, 8L, 12L, 
    6L, 22L, 14L, 1L, 2L, 11L, 2L, 19L, 12L, 3L, 19L, 8L, 6L, 
    20L, 22L, 1L, 6L, 2L, 8L, 13L, 10L, 8L, 21L, 1L, 16L, 20L, 
    11L, 20L, 13L, 14L, 22L, 12L, 21L, 17L, 7L, 24L, 12L, 7L, 
    22L, 10L, 13L, 3L), .Label = c("Agnes Gonzales", "Amber Waters", 
    "Amelia Gray", "Audrey Abbott", "Carla Hill", "Cesar Lynch", 
    "Derrick Johnson", "Donnie Hayes", "Elena Gordon", "Frank Barnes", 
    "Glenn Reynolds", "Herman Jensen", "Jonathan Mitchell", "Julian Brooks", 
    "Kristine Conner", "Lydia Maxwell", "Marianne Vega", "Marion Steele", 
    "Marlene Jones", "Marvin Klein", "Maurice Kennedy", "Pete Hicks", 
    "Ron Parks", "Ted Briggs", "Vernon Frank"), class = "factor"), 
    course = structure(c(22L, 22L, 22L, 21L, 14L, 22L, 16L, 22L, 
    22L, 22L, 9L, 4L, 18L, 19L, 19L, 19L, 14L, 22L, 22L, 7L, 
    23L, 23L, 10L, 1L, 14L, 14L, 22L, 1L, 21L, 22L, 4L, 16L, 
    4L, 22L, 21L, 24L, 12L, 21L, 1L, 22L, 22L, 22L, 21L, 14L, 
    22L, 16L, 22L, 22L, 22L, 9L, 4L, 18L, 19L, 19L, 19L, 14L, 
    22L, 22L, 7L, 23L, 23L, 10L, 1L, 14L, 14L, 22L, 1L, 21L, 
    22L, 4L, 16L, 4L, 22L, 21L, 24L, 12L, 21L, 1L, 22L, 22L, 
    6L, 21L, 22L, 18L, 22L, 14L, 22L, 22L, 22L, 9L, 19L, 16L, 
    7L, 19L, 1L, 24L, 12L, 14L, 21L, 4L, 19L, 22L, 22L, 19L, 
    22L, 21L, 22L, 21L, 22L, 4L, 22L, 14L, 1L, 22L, 23L, 23L, 
    4L, 22L, 10L, 4L, 21L, 17L, 3L, 22L, 1L, 4L, 22L, 21L, 4L, 
    5L, 1L, 2L, 14L, 8L, 15L, 24L, 3L, 4L, 14L, 22L, 22L, 2L, 
    11L, 21L, 13L, 22L, 21L, 22L, 23L, 4L, 20L), .Label = c("Applied Multivariate Analysis", 
    "Basic Applied Statistics", "Basic Probability Theory", "Basic Statistics for Economics", 
    "Caclulus I", "Computing and Graphics in Applied Statistics", 
    "english I", "english II", "Independent Studies in Statistics", 
    "Intermediate Statistical Analysis", "Introduction to Experimental Design", 
    "Introduction to Sampling", "Introductory Statistics for Business", 
    "Level II Statistics", "Level III Statistics. ", "Managerial Statistics", 
    "Regression Methods", "Reliability-Quality Control", "Statistical Quality Control", 
    "Statistics for Social Work", "Statistics I", "Statistics II", 
    "Theory of Probability", "Theory of Statistics"), class = "factor"), 
    season = structure(c(2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 
    2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 
    2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 
    1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 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, 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 = c("regular", "summer"
    ), class = "factor"), class_size = c(19L, 17L, 49L, 33L, 
    55L, 20L, 19L, 27L, 58L, 20L, 9L, 30L, 29L, 39L, 42L, 43L, 
    10L, 46L, 10L, 42L, 27L, 23L, 31L, 22L, 37L, 13L, 24L, 38L, 
    42L, 28L, 51L, 19L, 31L, 13L, 37L, 36L, 21L, 48L, 38L, 19L, 
    17L, 49L, 33L, 55L, 20L, 19L, 27L, 58L, 20L, 9L, 30L, 29L, 
    39L, 42L, 43L, 10L, 46L, 10L, 42L, 27L, 23L, 31L, 22L, 37L, 
    13L, 24L, 38L, 42L, 28L, 51L, 19L, 31L, 13L, 37L, 36L, 21L, 
    48L, 38L, 25L, 17L, 11L, 39L, 11L, 19L, 45L, 20L, 20L, 20L, 
    38L, 17L, 19L, 24L, 25L, 31L, 31L, 18L, 22L, 27L, 14L, 20L, 
    35L, 20L, 20L, 37L, 15L, 25L, 10L, 14L, 38L, 29L, 19L, 30L, 
    32L, 27L, 34L, 23L, 66L, 12L, 29L, 19L, 3L, 17L, 7L, 21L, 
    36L, 54L, 29L, 45L, 11L, 16L, 18L, 44L, 17L, 21L, 20L, 24L, 
    5L, 42L, 30L, 19L, 11L, 29L, 15L, 37L, 10L, 24L, 26L, 12L, 
    48L, 51L, 27L), class = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("high", 
    "low", "medium"), class = "factor")), .Names = c("native_speaker", 
"course_instructor", "course", "season", "class_size", "class"
), class = "data.frame", row.names = c(NA, -151L))