This is the code I tried,
library(e1071)
library(caret)
library(ROCR)
data <- read.csv("data.csv", header = TRUE)
set.seed(998)
inTraining <- createDataPartition(data$Class, p = .70, list = FALSE)
training <- data[ inTraining,]
testing <- data[-inTraining,]
while(length(data)>0){
## Building
On 1/1/19 5:31 PM, Priyanka Purkayastha wrote:
Thankyou David.. I tried the same, I gave x as the data matrix and y
as the class label. But it returned an empty "featureRankedList". I
get no output when I try the code.
If you want people to spend time on this you should post a reproducible
Thankyou David.. I tried the same, I gave x as the data matrix and y as the
class label. But it returned an empty "featureRankedList". I get no output
when I try the code.
On Tue, 1 Jan 2019 at 11:42 PM, David Winsemius
wrote:
>
> On 1/1/19 4:40 AM, Priyanka Purkayastha wrote:
> > I have a datas
Please keep communications on-list.
On 1/2/19 10:57 AM, Marc Girondot wrote:
Le 01/01/2019 à 22:35, Rolf Turner a écrit :
On 1/2/19 9:35 AM, Marc Girondot wrote:
Hello members of the list,
I asked 3 days ago a question about "how to get the SE of all effects
after a glm or glmm". I post he
On 1/2/19 9:35 AM, Marc Girondot wrote:
Hello members of the list,
I asked 3 days ago a question about "how to get the SE of all effects
after a glm or glmm". I post here a synthesis of the answer and a new
solution:
For example:
x <- rnorm(100)
y <- rnorm(100)
G <- as.factor(sample(c("A"
On 1/1/19 4:40 AM, Priyanka Purkayastha wrote:
I have a dataset (data) with 700 rows and 7000 columns. I am trying to do
recursive feature selection with the SVM model. A quick google search
helped me get a code for a recursive search with SVM. However, I am unable
to understand the first part
I have a dataset (data) with 700 rows and 7000 columns. I am trying to do
recursive feature selection with the SVM model. A quick google search
helped me get a code for a recursive search with SVM. However, I am unable
to understand the first part of the code, How do I introduce my dataset in
the c
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