eval（expr、envir、enclose）中出错：对象'；辊式皮带&x27；未在predict中找到

在调用中，在代码末尾，发送到：

predict(pml_training_rf_model_1, pml_validation$classe)

我得到一个错误：

eval（expr、envir、enclose）中出错：找不到对象“roll\u belt”

那是因为我应该这样调用函数：

predict(pml_training_rf_model_1, pml_validation)

“roll_belt”属性确实出现在我正在使用的数据帧中，因此我显然犯了一些其他错误，现在已更正并保存给后代

#Start code

rm(list=ls())
library("caret")
library("data.table")
library("randomForest")

set.seed(12345)
pml_training_file <- "pml-training.csv"
pml_testing_file <- "pml-testing.csv"

if (!file.exists(pml_training_file)) {
  pml_training_url <- "http://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"
  download.file(pml_training_url, pml_training_file)
}

pml_testing_file <- "pml-testing.csv"
if (!file.exists(pml_testing_file)) {
  pml_testing_url <- "http://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"
  download.file(pml_testing_url, pml_testing_file)
}

pml_training_original <- fread(pml_training_file, na.strings=c("NA","#DIV/0!",""), data.table = FALSE, stringsAsFactors = TRUE)

partition_index <- createDataPartition(y=pml_training_original$classe, p=0.6, list = FALSE)
pml_training <- pml_training_original[partition_index,]
pml_validation <-  pml_training_original[-partition_index,]

#Remove metadata columns
pml_training <- pml_training[,-c(1:7)]

#Remove columns where the number of NA results is above a given level
na_level = .75
nrow_pml_training = nrow(pml_training)
na_col_nums <- numeric()
for(i in 1:length(pml_training)) {
  sum_na = sum(is.na(pml_training[, i]))
  if(sum_na/nrow_pml_training >= na_level ) {
    na_col_nums <- c(na_col_nums, i)           
  }
}
pml_training <- pml_training[-na_col_nums]

#Set the columns in the validation data to be the same as those in the training data
pml_training_colnames <- colnames(pml_training)
pml_validation <- pml_validation[, pml_training_colnames]

pml_training_rf_model_1 <- randomForest(classe ~ ., data=pml_training)
#Wrong! pml_training_predictions_1 <- predict(pml_training_rf_model_1, pml_validation$classe)
pml_training_predictions_1 <- predict(pml_training_rf_model_1, pml_validation)
confusionMatrix(pml_validation$classe, pml_training_predictions_1)

#开始代码
rm（list=ls（））
库（“插入符号”）
库（“数据表”）
图书馆（“森林”）
种子集（12345）
pml_培训_文件正确代码为：

pml_training_rf_model_1 <- randomForest(classe ~ ., data=pml_training)
pml_training_predictions_1 <- predict(pml_training_rf_model_1, pml_validation)
confusionMatrix(pml_validation$classe, pml_training_predictions_1)

pml_培训\u rf_型号\u 1证明滚轴皮带在pml_验证$classe中。因为我相信R，当它说它不存在的时候。你需要提供一个使它失败的输入数据的例子，这样我们就可以重现和测试你的问题。谢谢@Irnzcig，输入数据加载也包含在代码示例中。