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.
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Hello stackoverflow community,
Im working on a uni-project in which we try to create Bayesian Network Classifier from data in R.
Ideally the classifier should be based on a General Bayesian Network (GNB) or a BN Augmented Naive Bayes(BAN).
Unfortunately Im yet to find a suitabel package to create either of those nets in R.
My research led me to the following two packages:
bnclassify, the most prominent package for BN classification, doesnt include GNBs or BANs at all.
bnlearn offers the possibility to learn GNBs but according to the creator the learning is focused on returning the correct dependence structure rather than maximizing the predictive accuracy for classification. I've tried to use them for my classification problem nonetheless but the result was underwhelming.
So my question is if anyone knows a R package to classify with GNBs or BANs
OR how to work with the GNBs fron bnlearn to improve their predictive accuracy for classification problems.
Thanks you for your help in advance.
Best Regards
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 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.
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 was wondering if the functionality given by Weka of building Model trees like M5P which has regression models in the leaves is possible in R. I know there is a way to handle it using the RWeka package. What was somehow strange to me is that the functionality does not exist in other R packages like rpart. The only way to get a "Model Tree" is using the Rweka package?
Thanks for clarification.
Please check cubist and CORElearn packages.