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Most Popular Learners in mlr

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
classif.naiveBayes Naive Bayes e1071 161085
classif.svm Support Vector Machines (libsvm) e1071 161085
regr.svm Support Vector Machines (libsvm) e1071 161085
classif.lda Linear Discriminant Analysis MASS 127165
classif.qda Quadratic Discriminant Analysis MASS 127165

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
classif.naiveBayes Naive Bayes e1071 161085
classif.svm Support Vector Machines (libsvm) e1071 161085
classif.lda Linear Discriminant Analysis MASS 127165
classif.qda Quadratic Discriminant Analysis MASS 127165
classif.gausspr Gaussian Processes kernlab 125347
classif.ksvm Support Vector Machines kernlab 125347
classif.lssvm Least Squares Support Vector Machine kernlab 125347
cluster.kkmeans Kernel K-Means kernlab 125347
regr.rvm Relevance Vector Machine kernlab 125347
surv.coxph Cox Proportional Hazard Model survival 103293
classif.plsdaCaret Partial Least Squares (PLS) Discriminant Analysis caret,pls 86534
regr.pcr Principal Component Regression pls 86534
regr.plsr Partial Least Squares Regression pls 86534
classif.randomForest Random Forest randomForest 71639
classif.rpart Decision Tree rpart 60921
surv.rpart Survival Tree rpart 60921
cluster.dbscan DBScan Clustering fpc 57836
classif.cvglmnet GLM with Lasso or Elasticnet Regularization (Cross Validated Lambda) glmnet 48517
classif.glmnet GLM with Lasso or Elasticnet Regularization glmnet 48517
surv.cvglmnet GLM with Regularization (Cross Validated Lambda) glmnet 48517

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

Final table

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

Here are the first 20 rows of the table:

package downloads learners
e1071 161085 classif.naiveBayes,classif.svm,regr.svm
MASS 127165 classif.lda,classif.qda
kernlab 125347 classif.gausspr,classif.ksvm,classif.lssvm,cluster.kkmeans,regr.gausspr,regr.ksvm,regr.rvm
survival 103293 surv.coxph
caret,pls 86534 classif.plsdaCaret
pls 86534 regr.pcr,regr.plsr
randomForest 71639 classif.randomForest,regr.randomForest
rpart 60921 classif.rpart,regr.rpart,surv.rpart
fpc 57836 cluster.dbscan
glmnet 48517 classif.cvglmnet,classif.glmnet,regr.cvglmnet,regr.glmnet,surv.cvglmnet,surv.glmnet
FNN 34957 classif.fnn,regr.fnn
party 28396 classif.cforest,classif.ctree,multilabel.cforest,regr.cforest,regr.ctree
party,modeltools 28396 regr.mob
party,survival 28396 surv.cforest
xgboost 27209 classif.xgboost,regr.xgboost
klaR 25405 classif.rda
gbm 22063 classif.gbm,regr.gbm,surv.gbm
h2o 19899 classif.h2o.deeplearning,classif.h2o.gbm,classif.h2o.glm,classif.h2o.randomForest,regr.h2o.deeplearning,regr.h2o.gbm,regr.h2o.glm,regr.h2o.randomForest
RWeka 19631 classif.adaboostm1,classif.IBk,classif.J48,classif.JRip,classif.OneR,classif.PART,cluster.Cobweb,cluster.EM,cluster.FarthestFirst,cluster.SimpleKMeans,cluster.XMeans,regr.IBk
nnet 19089 classif.multinom,classif.nnet,regr.nnet

And of course we want to have a small visualization:

lrns.pgks$learners = factor(lrns.pgks$learners, lrns.pgks$learners)
g = ggplot(lrns.pgks[20:1], aes(x = fct_inorder(stri_sub(
  paste0(package,": ",learners), 0, 64)), y = downloads, fill = downloads))
g + geom_bar(stat = "identity") + 
  coord_flip() + 
  xlab("") + 


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.