In guillermozbta/mir: Machine Learning in R

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)
data(iris)

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

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

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

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

## Get the mean misclassification error:
r$aggr

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:

parsePkgs = function(x) {
  x = strsplit(x, "\n|,")[[1L]]
  # remove version requirement in (...)
  x = sub("\\(.*\\)", "", x)
  # trim whitespace (cannot be inside name)
  x = gsub(" ", "", x)
  # empty string become char(0)
  x[nzchar(x)]
}

desc = packageDescription("mlr")
pkgs = c(parsePkgs(desc$Depends), parsePkgs(desc$Imports), parsePkgs(desc$Suggests))
pkgs = sort(setdiff(pkgs, c("R", "stats", "methods", "utils")))
cat(sprintf("* [%1$s](https://cran.r-project.org/package=%1$s)", pkgs), sep = "\n")