Question

我在没有找到解决方案的情况下进行了广泛的研究。我按如下方式清理了我的数据集：

library("raster")
impute.mean <- function(x) replace(x, is.na(x) | is.nan(x) | is.infinite(x) , 
mean(x, na.rm = TRUE))
losses <- apply(losses, 2, impute.mean)
colSums(is.na(losses))
isinf <- function(x) (NA <- is.infinite(x))
infout <- apply(losses, 2, is.infinite)
colSums(infout)
isnan <- function(x) (NA <- is.nan(x))
nanout <- apply(losses, 2, is.nan)
colSums(nanout)

问题出现了运行预测算法：

options(warn=2)
p  <-   predict(default.rf, losses, type="prob", inf.rm = TRUE, na.rm=TRUE, nan.rm=TRUE)

所有的研究都说它应该是数据中的NA或Inf或NaN，但我找不到。我正在将数据和randomForest摘要提供给[删除]进行调查回溯并没有透露太多（对我而言）：

4: .C("classForest", mdim = as.integer(mdim), ntest = as.integer(ntest), 
       nclass = as.integer(object$forest$nclass), maxcat = as.integer(maxcat), 
       nrnodes = as.integer(nrnodes), jbt = as.integer(ntree), xts = as.double(x), 
       xbestsplit = as.double(object$forest$xbestsplit), pid = object$forest$pid, 
       cutoff = as.double(cutoff), countts = as.double(countts), 
       treemap = as.integer(aperm(object$forest$treemap, c(2, 1, 
           3))), nodestatus = as.integer(object$forest$nodestatus), 
       cat = as.integer(object$forest$ncat), nodepred = as.integer(object$forest$nodepred), 
       treepred = as.integer(treepred), jet = as.integer(numeric(ntest)), 
       bestvar = as.integer(object$forest$bestvar), nodexts = as.integer(nodexts), 
       ndbigtree = as.integer(object$forest$ndbigtree), predict.all = as.integer(predict.all), 
       prox = as.integer(proximity), proxmatrix = as.double(proxmatrix), 
       nodes = as.integer(nodes), DUP = FALSE, PACKAGE = "randomForest")
3: predict.randomForest(default.rf, losses, type = "prob", inf.rm = TRUE, 
       na.rm = TRUE, nan.rm = TRUE)
2: predict(default.rf, losses, type = "prob", inf.rm = TRUE, na.rm = TRUE, 
       nan.rm = TRUE)
1: predict(default.rf, losses, type = "prob", inf.rm = TRUE, na.rm = TRUE, 
       nan.rm = TRUE)

Answer 1

您的代码不是完全可重现的（没有运行实际的randomForest算法）但您不用列向量替换Inf值。这是因为na.rm = TRUE函数中对mean()的调用中的impute.mean参数与其说的完全相同 - 删除了NA个值（而不是Inf个值）。

您可以通过以下方式看到这一点：

impute.mean <- function(x) replace(x, is.na(x) | is.nan(x) | is.infinite(x), mean(x, na.rm = TRUE))
losses <- apply(losses, 2, impute.mean)
sum( apply( losses, 2, function(.) sum(is.infinite(.))) )
# [1] 696

要摆脱无限值，请使用：

impute.mean <- function(x) replace(x, is.na(x) | is.nan(x) | is.infinite(x), mean(x[!is.na(x) & !is.nan(x) & !is.infinite(x)]))
losses <- apply(losses, 2, impute.mean)
sum(apply( losses, 2, function(.) sum(is.infinite(.)) ))
# [1] 0

Answer 2

错误消息的一个原因：

外部函数调用中的NA / NaN / Inf（arg X）

训练randomForest时，data.frame中有character个类变量。如果它附带警告：

强制引入的NAs

检查以确保所有字符变量都已转换为因子。

示例

set.seed(1) dat <- data.frame( a = runif(100), b = rpois(100, 10), c = rep(c("a","b"), 100), stringsAsFactors = FALSE ) library(randomForest) randomForest(a ~ ., data = dat)

收率：

randomForest.default（m，y，...）出错：外国人的NA / NaN / Inf 函数调用（arg 1）另外：警告消息：在data.matrix（x）中：强制引入的NAs

但是删除stringsAsFactors = FALSE参数并运行。

如何消除“外部函数调用（arg 7）中的NA / NaN / Inf”运行使用randomForest进行预测

2 个答案: