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Questions tagged [mlr]

mlr is a machine learning package for R that provides an interface to many other packages.

mlr is an R package that provides a standardized API to many of R's machine learning packages. On top of that, it offers resampling, feature selection, automatic tuning, cost-sensitive learning and much more. Its website can be found at https://github.com/mlr-org/mlr/

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How to interpret the variable importance plot produced via randomForestSRC::vimp?

This is a question directly related to the answer provided here: MLR random forest multi label get feature importance To summarize, the question is about producing a variable importance plot for a multi-label classification problem. I am coping the…
Miranda
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MLR random forest multi label get feature importance

I am using multilabel.randomForestSRC learner from mlr package for a multi-label classification problem I would like to return the variables importances The getFeatureImportance function return this issue : code: getFeatureImportance(mod) Error:…
Mathilde
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Custom performance measure when building models with mlr-package

I have just made the switch from caret to mlr for a specific problem I am working on at the moment. I am wondering if anyone here is familiar with specifying custom performance measures within the resample() function. Here's a reproducible code…
veghokstvd
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error : argument "x" is missing, with no default?

As im very new to XGBoost, I am trying to tune the parameters using mlr library and model but after using setHayperPars() learning using train() throws an error (in particular when i run xgmodel line): Error in colnames(x) : argument "x" is missing,…
Jude83
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R-MLR : tuning hyper parameters using ' makeTuneControlRandom ' for a wrapped learner

Following my previous question and recommendations addressed in its comments, I was trying to find a proper value for the maxit argument of the makeTuneControlRandom function so that when I shrink the lower:upper interval the optimized hyper…
Basilique
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MLR: Function "predict.WrappedModel" not found

I am using R 3.6.1, RStudio 1.2.5019 and mlr 2.15.0. Mlr ist installed and loaded. Only mlr and the packages mlr is built on are loaded. Now, I have trained a model using train and would like to test it on new data. Therefore, I want to use the…
TimoK
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mlr: Unexpected result when comparing output from getFilteredFeatures and generateFilterValuesData

I am using two different methods to get the features selected by a filter. I expected these methods to return the same values but they are not doing so and I do not understand why. The reason for using the second method is so that I can access the…
panda
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Deriving prediction efficiency and prediction errors for Ensemble Machine Learning model stacks

I am trying to derive prediction errors for ensemble models fitted using makeStackedLearner in the mlr package. These are the steps I am following: > library(mlr) > library(matrixStats) > data(BostonHousing, package = "mlbench") > tsk =…
Tom Hengl
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mlr: using a learner as a filter

I want to create a custom filter that uses the LASSO method (glmnet with alpha=1) to select features - i.e. the features to which glmnet assigns non-zero coefficients are the selected features. The reason I want glmnet as a filter is so that I can…
panda
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MLR, example dependent cost of misclassification, makeCostSensWeightedPairsWrapper

This question has been seen 74 times and has received only one response (as of noon (PDT) Wed, Aug-14). I've rewritten the question to make it as clear as possible and I'll appreciate any help. As a summary, I need a small but complete example on a…
dnqxt
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In mlr and parallelMap it possible to parallelize multiple levels? For example mlr.tuneParams and mlr.benchmark

I'm running a mlr benchmark with multiple learners (around 15 different learners) with nested resampling using the irace tuning control. My question is: is it possible to run two parallelization levels on parallelMap? If I use the mlr.benchmark…
Lucas Teixeira
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R: Predicting with new factor levels in mlr with regr.svm task

I am using the mlr package to predict from an SVM. If my validation set contains factor levels not present in my training data, the prediction fails, regardless of how I set fix.factors.prediction when making the SVM learner. What is the proper way…
coletl
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Tuning without resampling in mlr package (clustering)

In the mlr package, I can perform a clustering. Let´s say I don´t want to know how the model performs on unseen data, but I just want to know what the best number of clusters are regarding a given performance measure. In this example, I use the…
Banjo
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Blocking in cross validation in mlr with subject id

I have a dataset with multiple observations by participant. Participants are denoted by id. To account for this in the cross validation process, I add blocking = factor(id) to makeClassifTask() and blocking.cv = TRUE to makeResampleDesc(). However,…
00schneider
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How to make the benchmark function not to fail if a specific learner fails on a specific task?

I need to compare various learners on multiple tasks (> 100 000 hourly temperature records from weather stations) in a benchmark experiment. Because one of my learners may sometimes fails (and it is acceptable) I need a solution (I guess with…
pokyah
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