Most Popular Learners in mlr

March 29, 2017

(This article was first published on mlr-org, and kindly contributed to R-bloggers)

For the development of mlr as well as for an “machine learning expert” it can be handy to know what are the most popular learners used.
Not necessarily to see, what are the top notch performing methods but to see what is used “out there” in the real world.
Thanks to the nice little package cranlogs from metacran you can at least get a slight estimate as I will show in the following…

First we need to install the cranlogs package using devtools:


Now let’s load all the packages we will need:


Do obtain a neat table of all available learners in mlr we can call listLearners().
This table also contains a column with the needed packages for each learner separated with a ,.

# obtain used packages for all learners
lrns =
all.pkgs = stri_split(lrns$package, fixed = ",")

Note: You might get some warnings here because you likely did not install all packages that mlr suggests – which is totally fine.

Now we can obtain the download counts from the rstudio cran mirror, i.e. from the last month.
We use data.table to easily sum up the download counts of each day.

all.downloads = cran_downloads(packages = unique(unlist(all.pkgs)), when = "last-month")
all.downloads =
monthly.downloads = all.downloads[, list(monthly = sum(count)), by = package]

As some learners need multiple packages we will use the download count of the package with the least downloads.

lrn.downloads = sapply(all.pkgs, function(pkgs) {
  monthly.downloads[package %in% pkgs, min(monthly)]

Let’s put these numbers in our table:

lrns$downloads = lrn.downloads
lrns = lrns[order(downloads, decreasing = TRUE),]
lrns[, .(class, name, package, downloads)]

Here are the first 5 rows of the table:

class name package downloads
surv.coxph Cox Proportional Hazard Model survival 153681
classif.naiveBayes Naive Bayes e1071 102249
classif.svm Support Vector Machines (libsvm) e1071 102249
regr.svm Support Vector Machines (libsvm) e1071 102249
classif.lda Linear Discriminant Analysis MASS 55852

Now let’s get rid of the duplicates introduced by the distinction of the type classif, regr and we already have our…

nearly final table

lrns.small = lrns[, .SD[1,], by = .(name, package)]
lrns.small[, .(class, name, package, downloads)]

The top 20 according to the rstudio cran mirror:

class name package downloads
surv.coxph Cox Proportional Hazard Model survival 153681
classif.naiveBayes Naive Bayes e1071 102249
classif.svm Support Vector Machines (libsvm) e1071 102249
classif.lda Linear Discriminant Analysis MASS 55852
classif.qda Quadratic Discriminant Analysis MASS 55852
classif.randomForest Random Forest randomForest 52094
classif.gausspr Gaussian Processes kernlab 44812
classif.ksvm Support Vector Machines kernlab 44812
classif.lssvm Least Squares Support Vector Machine kernlab 44812
cluster.kkmeans Kernel K-Means kernlab 44812
regr.rvm Relevance Vector Machine kernlab 44812
classif.cvglmnet GLM with Lasso or Elasticnet Regularization (Cross Validated Lambda) glmnet 41179
classif.glmnet GLM with Lasso or Elasticnet Regularization glmnet 41179
surv.cvglmnet GLM with Regularization (Cross Validated Lambda) glmnet 41179
surv.glmnet GLM with Regularization glmnet 41179
classif.cforest Random forest based on conditional inference trees party 36492
classif.ctree Conditional Inference Trees party 36492
regr.cforest Random Forest Based on Conditional Inference Trees party 36492
regr.mob Model-based Recursive Partitioning Yielding a Tree with Fitted Models Associated with each Terminal Node party,modeltools 36492
surv.cforest Random Forest based on Conditional Inference Trees party,survival 36492

As we are just looking for the packages let’s compress the table a bit further and come to our…

final table

lrns[,list(learners = paste(class, collapse = ",")),by = .(package, downloads)]

Here are the first 20 rows of the table:

package downloads learners
survival 153681 surv.coxph
e1071 102249 classif.naiveBayes, classif.svm, regr.svm
MASS 55852 classif.lda, classif.qda
randomForest 52094 classif.randomForest, regr.randomForest
kernlab 44812 classif.gausspr, classif.ksvm, classif.lssvm, cluster.kkmeans, regr.gausspr, regr.ksvm, regr.rvm
glmnet 41179 classif.cvglmnet, classif.glmnet, regr.cvglmnet, regr.glmnet, surv.cvglmnet, surv.glmnet
party 36492 classif.cforest, classif.ctree, multilabel.cforest, regr.cforest, regr.ctree
party,modeltools 36492 regr.mob
party,survival 36492 surv.cforest
fpc 33664 cluster.dbscan
rpart 28609 classif.rpart, regr.rpart, surv.rpart
RWeka 20583 classif.IBk, classif.J48, classif.JRip, classif.OneR, classif.PART, cluster.Cobweb, cluster.EM, cluster.FarthestFirst, cluster.SimpleKMeans, cluster.XMeans, regr.IBk
gbm 19554 classif.gbm, regr.gbm, surv.gbm
nnet 19538 classif.multinom, classif.nnet, regr.nnet
caret,pls 18106 classif.plsdaCaret
pls 18106 regr.pcr, regr.plsr
FNN 16107 classif.fnn, regr.fnn
earth 15824
neuralnet 15506 classif.neuralnet
class 14493 classif.knn, classif.lvq1


This is not really representative of how popular each learner is, as some packages have multiple purposes (e.g. multiple learners).
Furthermore it would be great to have access to the trending list.
Also most stars at GitHub gives a better view of what the developers are interested in.
Looking for machine learning packages we see there e.g: xgboost, h2o and tensorflow.

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