On 10/29/15, 11:46 AM, "Lorenzo Isella" wrote:
>Dear All,
>I trained a model, let's call it mm, using caret+Cubist.
>When I type summary(mm), the output is rather long.
>This is because a Cubist model is a long set of rules, partially
>reminiscent of a classification
The caret package (short for Classification And REgression
Training) attempts to streamline the process for creating
predictive models. The package contains tools for:
- data splitting
- pre-processing
- feature selection
- model tuning using resampling (with parallel processing)
- variable
-Original Message-
From: Sarah Goslee [mailto:[EMAIL PROTECTED]
Sent: Monday, April 14, 2008 2:32 PM
To: Tobias Sing
Cc: r-help; Kuhn, Max
Subject: Re: [R] odfWeave: in multi-page plots only last page appears in
document
If you ran that code outside ODFWeave, you'd only get one plot, so why
would you
?which
which(X)
[1] 1 2 3
Max
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On Behalf Of Barb, Jennifer (NIH/CIT) [E]
Sent: Tuesday, November 20, 2007 3:07 PM
To: [EMAIL PROTECTED]
Subject: [R] Loc function in R??
Does anyone know which function (if any) will
Graham,
I'm not sure what the issue is, but I would suggest using OpenOffice
directly with the odfWeave package.
Max
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On Behalf Of Graham Smith
Sent: Monday, November 05, 2007 3:02 AM
To: [EMAIL PROTECTED]
Subject: [R]
Sancar,
I'm trying to use RWeka to use a NaiveBayes Classifier(the Weka
version). However it crashes whenever there is a NA in the class
Gender
It is difficult to tell what the problem is without the data (or a
reproducible toy example), but it seems like you are trying to remove NA
values
Julia,
i checked the caret package out and the tuning works. but i
can't find a way to make a contingency table in order to
see the classification result.
You should read the vignettes for the package at:
http://cran.r-project.org/src/contrib/Descriptions/caret.html
these have the
am trying to implement the code of the e1071 package for naive bayes,
but it doens't really work, any ideas??
am very glad about any help!!
need a naive bayes with 10-fold cross validation:
The caret package will do this. Use
fit - train(
x, y, method = nb,
trControl =
Dave,
I have been using random forest on a data set with 226 sites and 36
explanatory variables (continuous and categorical). When I use
tune.randomforest to determine the best value to use in mtry there
is a fairly consistent and steady decrease in MSE, with the optimum of
mtry usually
Howard,
though it received the statement. I tried to use some R built-in
functions of try, tryCatch, eval, expression, as.expression,
parse, deparse, etc. None of them worked.
How did they not work? What did you attempt? Did you use silent = TRUE
in try?
Please be more specific about the
Three more packages will be showing up on your mirror soon.
The caret package (short for Classification And REgression Training)
aims to simplify the model building process. The package has functions
for
- data splitting: balanced train/test splits, cross-validation and
bootstrapping sampling
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