I'm looking to set up SVM RFE and found the SO article here: Implementation of SVM-RFE Algorithm in R
It appears that the SVM function in the code is different from that in the e1071 library and i've been looking around to find the library with the function referenced in the above article, with no luck. The parameters in the example linked above appear different from what's used the e1071 library and i'm hoping someone out there might have some insight. I've been looking around a lot without any luck in tracking down the difference between "SVM()" in the example and "svm()" from e1071.
Related
I am trying to replicate the output obtained in SPSS doing it in R but I do not get the same results. I used the following libraries to help me, but I didn't get the same results as SPSS:
library ("rpart")
library ("readr")
library ("caTools")
library ("dplyr")
library ("party")
library ("party kit")
library ("rpart.plot")
Does anyone know how I could do it? From my research I discovered that at one point there was a library called "CHAID" but now it is no longer available.
The question says it all. I am wondering whether there is an implementation of this specific regionalization algorithm in R. I found implementations of the SKATER and REDCAP algorithms but not the max-p region model.
I found one in using rgeoda - an R package GeoDa
I'm currently dealing with multivariate dynamic time warping (DTW) in R. The best library I found so far is the dtw package as described here: http://dtw.r-forge.r-project.org/
But I do not know how multivariate dtw is actually implemented and it is also not described in the description of the package. All in all, I would like to know if it implements DTWD (Dependent DTW) or DTWI (Independent DTW).
Does anyone have an idea or a suggestion how to find out which of these two approaches the package uses? Or are there libaries which allow me to choose the variant?
You can always look at the companion papers that come with the packages for a more theoretical grounding of the packages
https://www.jstatsoft.org/article/view/v031i07
Additionally, you can always look into the "guts" of the functions to understand how things are implemented in the code
https://www.rdocumentation.org/packages/dtw/versions/1.20-1/source
As far as this question in general, perhaps it is better suited for https://stats.stackexchange.com/ where the questions are more methodological than programming.
The two types of multivariate dynamic time warping are carefully explained here
https://www.cs.ucr.edu/~eamonn/Multi-Dimensional_DTW_Journal.pdf
In the caret package, which ensemble models can be used for multi class classification?
Also on trying some of the functions mentioned in http://topepo.github.io/caret/Ensemble_Model.html it is giving:
Not in caret's built-in library.
There are no suggestions of relevant packages for many functions on Google either. Could anyone kindly help me out with both these questions?
Most of them can (assuming that they are not solely regression models). We've listed the exclusions here
Here you can see an overview that also lists packages needed.
I wonder if anyone knows how to parallelize rfcv() function implemented in R-package 'randomForest'. Sorry if the question sounds very basic, but I tried to do this using 'foreach' without any results.
Have a look at the caret package and its documentation.
It not only is more general (allowing for more models than "just" random forests) but also integrates pre- and post-processing --- while also giving you parallel execution where feasible, particularly for evaluation and cross-validation which is an "embarassingly parallel" problem.