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.
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I use tensorflow to classify my objects in R. I would like to load the model (*.hdf5) and make predictions without the annoying tensorflow messages popping up.
I know how to disable this in python, but not in R. I did not find an R solution in tensorflow & keras documentation.
Would appreciate help.
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 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
Has anyone tried using package dlmodeler which seems to provide an equivalent of PROC UCM (SAS). Using structTS function is equal to running univariate PROC UCM. Can anyone tell me how to do a multivariate analysis using the same.
Built a package which runs unobserved components model in R which depends on the KFAS package.
The package is available on github.