Thank you, Tony.
Even in 2012, I still found your post useful.
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Hi,
I was using SPLS package to do multi-class classification and would require
probabilities to be output for each class.
In the vignette, the documentation does say that you can output
probabilities by requesting fit.type = response, however I still only get
predicted classes, rather than the
Hi,
The work around this is to use printRandomForests function in Rattle
package. It outputs the forest in the form of rules.
For ex:
#Load libraries
library(rattle)
library(randomForest)
#Load sample data
data(iris)
#Build a forest
iris.rf - randomForest(Species ~ ., data=iris,
Hi,
I'm trying to extract the rules from every tree in the random forest model
that I've created. I've used randomForest2Rules function from rattle
package however I get an error Error in if (var.class == character |
var.class == factor) { :
argument is of length zero
Sample code:
You could treat the dependent variable as a nominal variable. And scale the
indepent variables to have a Mean:0 and StDev:1. Stick all these in a
multinomial regression package such as mlogit. Or a non -parametric method
such as randomForest.
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No, you do not need to install anything except the SQLDF package. I've used
it to do joins on Rdataframe. It worked well for me.
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Marc, Many thanks.
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Try the below code:
==
library(foreach)
library(doSNOW)
getDoParWorkers()
getDoParName()
registerDoSNOW(makeCluster(2, type = SOCK))
getDoParWorkers()
getDoParName()
testFun - function(m) {
out - m*m
}
out - foreach(m=1:10, .combine=rbind,*.verbose=T*) %dopar%
Hello All,
I've created a function as follows so as to use it in a loop.
freq-function(i)
{
library(RODBC)
paste(i,-sqlQuery(conn,,',select click_flg, open_flg,, i , from
modeling_5'),sep=)
}
freq(i=AQI)
[1] AQI-sqlQuery(conn,'select click_flg, open_flg,AQI from modeling_5')
What I was
Hi,
I have a 1,785,421 rows x200 variables dataset with some missing values.
Approximately 55% of 1,785,421x200 are missing cells.
In this
ftp://ftp.stat.berkeley.edu/pub/users/breiman/Using_random_forests_v4.0.pdf
document , it is claimed that random forests can impute with great accuracy
even
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