When mtry is equal to total number of features, you just get regular bagging
(in the R package -- Breiman & Cutler's Fortran code samples variable with
replacement, so you can't do bagging with that). There are cases when
bagging will do better than random feature selection (i.e., RF), even in
sim
Hello,
I've a question regarding randomForest (from the package with same name). I've
16 featurs (nominative), 159 positive and 318 negative cases that I'd like to
classify (binary classification).
Using the tuning from the e1071 package it turns out that the best performance
if reached when u
> From: Uwe Ligges
>
> [EMAIL PROTECTED] wrote:
>
> > Hello,
> >
> > I'm trying to find out the optimal number of splits (mtry parameter)
> > for a randomForest classification. The classification is binary and
> > there are 32 explanatory variables (mostly factors with each up to 4
> > levels bu
> From: [EMAIL PROTECTED]
>
> Hello,
>
> I'm trying to find out the optimal number of splits (mtry
> parameter) for a randomForest classification. The
> classification is binary and there are 32 explanatory
> variables (mostly factors with each up to 4 levels but also
> some numeric variables
See the tuneRF() function in the package for an implementation of
the strategy recommended by Breiman & Cutler.
BTW, "randomForest" is only for the R package. See Breiman's
web page for notice on trademarks.
Andy
> From: Weiwei Shi
>
> Hi,
> I found the following lines from Leo's randomFore
Hi,
I found the following lines from Leo's randomForest, and I am not sure
if it can be applied here but just tried to help:
mtry0 = the number of variables to split on at each node. Default is
the square root of mdim. ATTENTION! DO NOT USE THE DEFAULT VALUES OF
MTRY0 IF YOU WANT TO OPTIMIZE THE P
[EMAIL PROTECTED] wrote:
> Hello,
>
> I'm trying to find out the optimal number of splits (mtry parameter)
> for a randomForest classification. The classification is binary and
> there are 32 explanatory variables (mostly factors with each up to 4
> levels but also some numeric variables) and 575
Hello,
I'm trying to find out the optimal number of splits (mtry parameter) for a
randomForest classification. The classification is binary and there are 32
explanatory variables (mostly factors with each up to 4 levels but also some
numeric variables) and 575 cases.
I've seen that although th