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
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
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
[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
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
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 randomForest,
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) and
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 but also some
