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.
Related
I am currently fitting a penalized logistic regression model using the package logistf (due to quasi-complete separation).
I chose this package over brglm because I found much more recommendations for logistf. However, the brglm seems to integrate better with other functions such as predict() or margins::margins(). In the documentation of brglm it says:
"Implementations of the bias-reduction method for logistic regressions can also be found in thelogistf package. In addition to the obvious advantage ofbrglmin the range of link functions that can be used ("logit","probit","cloglog"and"cauchit"), brglm is also more efficient computationally."
Has anyone experience with those two packages and can tell me whether I am overlooking a weakness in brglm, or can I just use it instead of logistf?
I'd be grateful for any insights!
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.
I can't seem to find an example of parametertuning a neuralnet with the Caret package in R for a classification problem.
It seems like the method="neuralnet" only support regression problems.
Does anybody have a solution for my problem?
The list of models available in caret can be found here. Neural networks that support classification include mxnet and nnet.
I want to build a Bagged Logistic Regression Model in R. My dataset is really biased and has 0.007% of positive occurrences.
My thoughts to solve this was to use Bagged Logistic Regression. I came across the hybridEnsemble package in R. Does anyone have an example of how this package can be used? I searched online, but unfortunately did not find any examples.
Any help will be appreciated.
The way that I would try to solve this is use the h2o.stackedEnsemble() function in the h2o R package. You can automatically create more balanced classifiers by using the balance_classes = TRUE option in all of the base learners. More information about how to use this function to create ensembles is located in the Stacked Ensemble H2O docs.
Also, using H2O will be way faster than anything that's written in native R.
I'm really confused about regression models and functions in R. Here is my problem. I'm using the PLS package to make a model like Y~x. To do that I have to use 'plsr':
model=plsr(Y~X,ncomp=10,data=df1,center=TRUE, scale=TRUE, validation="LOO")
I couldn't find the source of the 'plsr' in the PLS source code but in the help document it says it refers to 'mvr{pls}' which I could find it. first is 'plsr' a function or model.. in the R terminology? is it built in R? and how does it refer to 'mvr' function in pls package?
Thanks