Author: Bernd Bischl, Michel Lang, Jakob Richter, Jakob Bossek, Leonard Judt, Tobias Kuehn, Erich Studerus, Lars Kotthoff
Date: 2018-03-07

# mlr: Machine Learning in R

#### Bernd Bischl, Michel Lang, Jakob Richter, Jakob Bossek, Leonard Judt, Tobias Kuehn, Erich Studerus, Lars Kotthoff

#### 2018-03-07

# mlr: Machine Learning in R

This Vignette is supposed to give you a short introductory glance at the key features of `mlr`. A more detailed in depth and continuously updated tutorial can be found on the GitHub project page:

- Project Page
- Online Tutorial for mlr release and mlr devel
- Download the online tutorial for mlr release and mlr devel as zip for offline usage

## Purpose

The main goal of `mlr` is to provide a unified interface for machine learning tasks as classification, regression, cluster analysis and survival analysis in R. In lack of a common interface it becomes a hassle to carry out standard methods like cross-validation and hyperparameter tuning for different learners. Hence, `mlr` offers the following features:

- Possibility to fit, predict, evaluate and resample models
- Easy extension mechanism through S3 inheritance
- Abstract description of learners and tasks by properties
- Parameter system for learners to encode data types and constraints
- Many convenience methods and generic building blocks for your machine learning experiments
- Resampling like bootstrapping, cross-validation and subsampling
- Different visualizations for e.g. ROC curves and predictions
- Benchmarking of learners for multiple data sets
- Easy hyperparameter tuning using different optimization strategies
- Variable selection with filters and wrappers
- Nested resampling of models with tuning and feature selection
- Cost-sensitive learning, threshold tuning and imbalance correction
- Wrapper mechanism to extend learner functionality and complex and custom ways
- Combine different processing steps to a complex data mining chain that can be jointly optimized
- Extension points to integrate your own stuff
- Parallelization is built-in

## Quick Start

To highlight the main principles of `mlr` we give a quick introduction to the package. We demonstrate how to simply perform a classification analysis using a stratified cross validation, which illustrates some of the major building blocks of the `mlr` workflow, namely tasks and learners.

    library(mlr)

    ## Loading required package: ParamHelpers

    data(iris)

    ## Define the task:
    task = makeClassifTask(id = "tutorial", data = iris, target = "Species")
    print(task)

    ## Supervised task: tutorial
    ## Type: classif
    ## Target: Species
    ## Observations: 150
    ## Features:
    ##    numerics     factors     ordered functionals
    ##           4           0           0           0
    ## Missings: FALSE
    ## Has weights: FALSE
    ## Has blocking: FALSE
    ## Has coordinates: FALSE
    ## Classes: 3
    ##     setosa versicolor  virginica
    ##         50         50         50
    ## Positive class: NA

    ## Define the learner:
    lrn = makeLearner("classif.lda")
    print(lrn)

    ## Learner classif.lda from package MASS
    ## Type: classif
    ## Name: Linear Discriminant Analysis; Short name: lda
    ## Class: classif.lda
    ## Properties: twoclass,multiclass,numerics,factors,prob
    ## Predict-Type: response
    ## Hyperparameters:

    ## Define the resampling strategy:
    rdesc = makeResampleDesc(method = "CV", stratify = TRUE)

    ## Do the resampling:
    r = resample(learner = lrn, task = task, resampling = rdesc)

    ## Resampling: cross-validation

    ## Measures:             mmce

    ## [Resample] iter 1:    0.0000000

    ## [Resample] iter 2:    0.0000000

    ## [Resample] iter 3:    0.0666667

    ## [Resample] iter 4:    0.0666667

    ## [Resample] iter 5:    0.0000000

    ## [Resample] iter 6:    0.0000000

    ## [Resample] iter 7:    0.0666667

    ## [Resample] iter 8:    0.0000000

    ## [Resample] iter 9:    0.0000000

    ## [Resample] iter 10:   0.0000000

    ##

    ## Aggregated Result: mmce.test.mean=0.0200000

    ##

    print(r)

    ## Resample Result
    ## Task: tutorial
    ## Learner: classif.lda
    ## Aggr perf: mmce.test.mean=0.0200000
    ## Runtime: 0.0647471

    ## Get the mean misclassification error:
    r$aggr

    ## mmce.test.mean
    ##           0.02

## Detailed Tutorial

The previous example just demonstrated a tiny fraction of the capabilities of `mlr`. More features are covered in the tutorial which can be found online on the `mlr` project page. It covers among others: benchmarking, preprocessing, imputation, feature selection, ROC analysis, how to implement your own learner and the list of all supported learners. Reading is highly recommended!

- Project Page
- Online Tutorial for mlr release and mlr devel
- Offline Tutorial: online tutorial for mlr release and mlr devel as zip for offline usage
- Wiki with additional information for developers

## Thanks

We would like to thank the authors of all packages which `mlr` uses under the hood:

- BBmisc
- C50
- CoxBoost
- Cubist
- DiceKriging
- DiceOptim
- DiscriMiner
- FDboost
- FNN
- FSelector
- GPfit
- GenSA
- Hmisc
- LiblineaR
- MASS
- PMCMR
- ParamHelpers
- ROCR
- RRF
- RSNNS
- RWeka
- SwarmSVM
- TH.data
- XML
- ada
- adabag
- backports
- bartMachine
- batchtools
- brnn
- bst
- care
- caret
- checkmate
- clValid
- class
- clue
- cluster
- clusterSim
- cmaes
- crs
- data.table
- deepnet
- e1071
- earth
- elasticnet
- elmNN
- emoa
- evtree
- extraTrees
- fda.usc
- fields
- flare
- fpc
- frbs
- gbm
- ggplot2
- glmnet
- h2o
- ipred
- irace
- kernlab
- kknn
- klaR
- knitr
- laGP
- lintr
- lqa
- mRMRe
- mboost
- mco
- mda
- mlbench
- mldr
- mlrMBO
- mmpf
- modeltools
- neuralnet
- nnet
- nodeHarvest
- numDeriv
- pamr
- parallelMap
- party
- penalized
- pls
- rFerns
- randomForest
- randomForestSRC
- ranger
- refund
- rex
- rknn
- rmarkdown
- robustbase
- rotationForest
- rpart
- rrlda
- rsm
- sda
- shiny
- smoof
- sparseLDA
- stepPlr
- stringi
- survAUC
- survival
- svglite
- testthat
- tgp
- wavelets
- xgboost