Question

我正在尝试从插入符号包中实现rfe。我对X和Y的观察次数相同，但是不断出现错误。

x和y中的样本数应该相同

我的脚本如下

# Importing the dataset
dataset <- read.csv('P1training.csv')
dataset <- dataset[,2:ncol(dataset)]

# Splitting test and train dataset
library(caTools)
set.seed(123)
split <- sample.split(dataset$Appliances, SplitRatio = 0.8)
training_set <- subset(dataset, split == TRUE)
test_set <- subset(dataset, split == FALSE)

# Feature Scaling
training_set <- scale(training_set)
test_set <- scale(test_set)

train_X <- as.data.frame(training_set[,2:ncol(training_set)])
train_y <- as.data.frame(training_set[,1])

test_X <- as.data.frame(test_set[,2:ncol(test_set)])
test_y <- as.data.frame(test_set[,1])

nrow(test_X)
nrow(test_y)
nrow(train_X)
nrow(train_y)

# load the library
library(doParallel)
library(mlbench)
library(caret)
# define the control using a random forest selection function
control <- rfeControl(functions=nbFuncs, method="cv", number=1, allowParallel = TRUE)
# run the RFE algorithm
results <- rfe(x=train_X, y=train_y, sizes=c(1:5),testX=test_X, testY=test_y, rfeControl=control)
# summarize the results
print(results)
# list the chosen features
predictors(results)
# plot the results
plot(results, type=c("g", "o"))

观察计数

> nrow(test_X)
[1] 2958
> nrow(test_y)
[1] 2958
> nrow(train_X)
[1] 11845
> nrow(train_y)
[1] 11845

有人看过这个问题吗？任何帮助是极大的赞赏。谢谢。

Answer 1

根据?rfe参数y应该

训练集结果（数字或因子）的向量

您的y是train_y <- as.data.frame(training_set[,1])，即data.frame。因此，length (train_y)为1，不等于nrow(test_X)。

尝试使用：

results <- rfe(x=train_X, y=train_y[, 1], sizes=c(1:5),testX=test_X, testY=test_y, rfeControl=control)

caret-rfe：x和y中应该有相同数量的样本

1 个答案: