Hi,
Thanks for your help,
This worked very well:
na.action=na.roughfix
Kevin
On Sun, Feb 26, 2012 at 3:10 PM, Weidong Gu wrote:
> Hi,
>
> You can set na.action=na.roughfix which fills NAs with the mean or
> mode of the missing variable.
>
> Other option is to impute missing values using rfIm
Hi,
You can set na.action=na.roughfix which fills NAs with the mean or
mode of the missing variable.
Other option is to impute missing values using rfImpute, then run
randomForest on the complete data set.
Weidong Gu
On Sat, Feb 25, 2012 at 6:24 PM, kevin123 wrote:
> I am using the package Ran
On Feb 25, 2012, at 6:24 PM, kevin123 wrote:
I am using the package Random Forrest to test and train a model,
I aim to predict (LengthOfStay.days),:
library(randomForest)
model <- randomForest( LengthOfStay.days~.,data = training,
+ importance=TRUE,
+ keep.forest=TRUE
+ )
*This is a small
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