I'm trying to run a randomForest on a large-ish data set (5000x300). Unfortunately I'm getting an error message as follows:
> RF <- randomForest(prePrior1, postPrior1[,6]
+ ,,do.trace=TRUE,importance=TRUE,ntree=100,,forest=TRUE)
Error in randomForest.default(prePrior1, postPrior1[, 6], , do.trace = TRUE, :
NA/NaN/Inf in foreign function call (arg 1)
So I try to find any NA's using :
> df2 <- prePrior1[is.na(prePrior1)]
> df2
character(0)
> df2 <- postPrior1[is.na(postPrior1[,6])]
> df2
numeric(0)
which leads me to believe that it's Inf's that are the problem as there don't seem to be any NA's.
Any suggestions for how to root out Inf's?
Inf and -Inf stands for infinity (or negative infinity) and is a result of storing either a large number or a product that is a result of division by zero. Inf is a reserved word and is – in most cases – product of computations in R language and therefore very rarely a product of data import.
Inf and -Inf are positive and negative infinity whereas NaN means 'Not a Number'. (These apply to numeric values and real and imaginary parts of complex values but not to values of integer vectors.) Inf and NaN are reserved words in the R language.
replace([np. inf, -np. inf], 0, inplace=True)” is used and this will replace all negative and positive infinite value with zero in “Marks” column of Pandas dataframe.
You're probably looking for is.finite
, though I'm not 100% certain that the problem is Infs in your input data.
Be sure to read the help for is.finite
carefully about which combinations of missing, infinite, etc. it picks out. Specifically, this:
> is.finite(c(1,NA,-Inf,NaN))
[1] TRUE FALSE FALSE FALSE
> is.infinite(c(1,NA,-Inf,NaN))
[1] FALSE FALSE TRUE FALSE
One of these things is not like the others. Not surprisingly, there's an is.nan
function as well.
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