On Tue, 9 Jan 2007, Bálint Czúcz wrote:
There is an improved version of the original random forest algorithm
available in the party package (you can find some additional
information on the details here:
http://www.stat.uni-muenchen.de/sfb386/papers/dsp/paper490.pdf ).
I do not know whether it
There is an improved version of the original random forest algorithm
available in the party package (you can find some additional
information on the details here:
http://www.stat.uni-muenchen.de/sfb386/papers/dsp/paper490.pdf ).
I do not know whether it yields a solution to your problem about
Does anyone know a reason why, in principle, a call to randomForest
cannot accept a data frame with missing predictor values? If each
individual tree is built using CART, then it seems like this
should be possible. (I understand that one may impute missing values
using rfImpute or some other
-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Darin A. England
Sent: Thursday, January 04, 2007 3:13 PM
To: r-help@stat.math.ethz.ch
Subject: [R] randomForest and missing data
Does anyone know a reason why, in principle, a call to randomForest
cannot accept a data frame
to
know too.
Hugues Sicotte
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Darin A. England
Sent: Thursday, January 04, 2007 3:13 PM
To: r-help@stat.math.ethz.ch
Subject: [R] randomForest and missing data
Does anyone know a reason why
You can try randomForest in Fortran codes, which has that function
doing missing replacement automatically. There are two ways of
imputations (one is fast and the other is time-consuming) to do that.
I did it long time ago.
the link is below. If you have any question, just let me know.
