为什么 R 中的随机森林回归不喜欢我的输入数据？

Question

我正在尝试一个非常简单的随机森林，如下所示：代码完全独立且可运行。

library(randomForest)
n = 1000

factor=10
x1 = seq(n) + rnorm(n, 0, 150)
y = x1*factor + rnorm(n, 0, 550)

x_data = data.frame(x1)
y_data = data.frame(y)

k=2
for (nfold in seq(k)){
  fold_ids <- cut(seq(1, nrow(x_data)), breaks=k, labels=FALSE)
  id_indices <- which(fold_ids==nfold)
  fold_x <- x_data[id_indices,]
  fold_y <- y_data[id_indices,]
  fold_x_df = data.frame(x=fold_x)
  fold_y_df = data.frame(y=fold_y)
  print(paste("number of rows in fold_x_df is ", nrow(fold_x_df), sep=" "))
  print(paste("number of rows in fold_y_df is ", nrow(fold_y_df), sep=" "))
  rf = randomForest(fold_x_df, fold_y_df, ntree=1000)
  print(paste("mse for fold number  ", " is ", sum(rf$mse)))
}

rf = randomForest(x_data, y_data, ntree=1000)

它给我一个错误：

...The response has five or fewer unique values.  Are you sure you want to do regression?

我不明白为什么它会给我这个错误。

我检查了这些来源：

Use of randomforest() for classification in R? RandomForest error code https://www.kaggle.com/c/15-071x-the-analytics-edge-competition-spring-2015/forums/t/13383/warning-message-in-random-forest

None 解决了我的问题。你可以看看打印报表，显然有超过 5 个独特的标签。更何况，我这里做的是回归，不是分类，所以我不太清楚为什么错误中使用了"label"这个词。

Answer 1

问题是将响应作为数据框提供。由于响应必须是一维的，因此它应该是一个向量是有道理的。以下是我将如何简化您的代码以使用 randomForest 的 data 参数和 formula 方法来完全避免该问题：

  ## simulation: unchanged (but seed set for reproducibility)
library(randomForest)
n = 1000
factor=10
set.seed(47)
x1 = seq(n) + rnorm(n, 0, 150)
y = x1*factor + rnorm(n, 0, 550)

  ## use a single data frame
all_data = data.frame(y, x1)

  ## define the folds outside the loop
fold_ids <- cut(seq(1, nrow(x_data)), breaks = k, labels = FALSE)

for (nfold in seq(k)) {
    id_indices <- which(fold_ids == nfold)
      ## sprintf can be nicer than paste for "filling in blanks"
    print(sprintf("number of rows in fold %s is %s", nfold, length(id_indices)))
      ## just pass the subset of the data directly to randomForest
      ## no need for extracting, subsetting, putting back in data frames...
    rf <- randomForest(y ~ ., data = all_data[id_indices, ], ntree = 1000)
      ## sprintf also allows for formatting
      ## the %g will use scientific notation if the exponent would be >= 3
    print(sprintf("mse for fold %s is %g", nfold, sum(rf$mse)))
}

为什么 R 中的随机森林回归不喜欢我的输入数据？

Why does Random Forest regression in R not like my input data?

r

vector

dataframe

random-forest