mlr Tutorial
Machine Learning
2016-09-21 v1
Authors:
Julia Schiffner
, Bernd Bischl
, Michel Lang
, Jakob Richter
, Zachary M. Jones
, Philipp Probst
, Florian Pfisterer
, Mason Gallo
, Dominik Kirchhoff
, Tobias Kühn
, Janek Thomas
, Lars Kotthoff
Abstract
This document provides and in-depth introduction to the mlr framework for machine learning experiments in R.
Cite
@article{arxiv.1609.06146,
title = {mlr Tutorial},
author = {Julia Schiffner and Bernd Bischl and Michel Lang and Jakob Richter and Zachary M. Jones and Philipp Probst and Florian Pfisterer and Mason Gallo and Dominik Kirchhoff and Tobias Kühn and Janek Thomas and Lars Kotthoff},
journal= {arXiv preprint arXiv:1609.06146},
year = {2016}
}
Related papers
View all related →
Machine Learning · Statistics
OpenML: An R Package to Connect to the Machine Learning Platform OpenML
Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff +5
2020-07-15
Adaptation and Self-Organizing Systems · Physics
Machine Learning in Nonlinear Dynamical Systems
Sayan Roy, Debanjan Rana
2020-11-30
Machine Learning · Computer Science
TherML: Thermodynamics of Machine Learning
Alexander A. Alemi, Ian Fischer
2018-10-08
Machine Learning · Statistics
mlr3torch: A Deep Learning Framework in R based on mlr3 and torch
Sebastian Fischer, Lukas Burk, Carson Zhang, Bernd Bischl +1
2026-04-21
Quantum Physics · Physics
Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
Yuxuan Du, Xinbiao Wang, Naixu Guo, Zhan Yu +5
2025-02-04
Machine Learning · Computer Science
Machine Learning Testing: Survey, Landscapes and Horizons
Jie M. Zhang, Mark Harman, Lei Ma, Yang Liu
2019-12-24
Machine Learning · Computer Science
A Survey on Large-scale Machine Learning
Meng Wang, Weijie Fu, Xiangnan He, Shijie Hao +1
2020-08-11
Chemical Physics · Physics
Tutorial: How to Train a Neural Network Potential
Alea Miako Tokita, Jörg Behler
2023-10-13
Machine Learning · Computer Science
Technology Readiness Levels for Machine Learning Systems
Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic, Siddha Ganju +11
2023-01-11
Computation and Language · Computer Science
Machine Learning and Applied Linguistics
Sowmya Vajjala
2018-04-11
Machine Learning · Computer Science
Reinforcement Learning Applications
Yuxi Li
2019-08-21
Machine Learning · Computer Science
An Overview of Multi-Task Learning in Deep Neural Networks
Sebastian Ruder
2017-06-19
Machine Learning · Computer Science
Modular Machine Learning: An Indispensable Path towards New-Generation Large Language Models
Xin Wang, Haoyang Li, Haibo Chen, Zeyang Zhang +1
2025-09-19
Information Retrieval · Computer Science
Neural Networks for Information Retrieval
Tom Kenter, Alexey Borisov, Christophe Van Gysel, Mostafa Dehghani +2
2018-01-09
Machine Learning · Computer Science
An Introduction to Deep Reinforcement Learning
Vincent Francois-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare +1
2018-12-04
Machine Learning · Computer Science
Formal Algorithms for Transformers
Mary Phuong, Marcus Hutter
2022-07-26
Atmospheric and Oceanic Physics · Physics
Using Machine Learning for Model Physics: an Overview
Vladimir Krasnopolsky, Aleksei A. Belochitski
2022-06-22
Programming Languages · Computer Science
Deep R Programming
Marek Gagolewski
2024-08-28
Machine Learning · Computer Science
RMDL: Random Multimodel Deep Learning for Classification
Kamran Kowsari, Mojtaba Heidarysafa, Donald E. Brown, Kiana Jafari Meimandi +1
2018-06-01
Accelerator Physics · Physics
Opportunities in Machine Learning for Particle Accelerators
Auralee Edelen, Christopher Mayes, Daniel Bowring, Daniel Ratner +9
2018-11-09
Machine Learning · Computer Science
A Guide to Failure in Machine Learning: Reliability and Robustness from Foundations to Practice
Eric Heim, Oren Wright, David Shriver
2025-03-04
Software Engineering · Computer Science
Empowering the trustworthiness of ML-based critical systems through engineering activities
Juliette Mattioli, Agnes Delaborde, Souhaiel Khalfaoui, Freddy Lecue +2
2022-10-03
Computational Physics · Physics
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach +1
2022-03-15
Machine Learning · Statistics
DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R
Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler +1
2024-06-06
Computation and Language · Computer Science
Neural Machine Translation and Sequence-to-sequence Models: A Tutorial
Graham Neubig
2017-03-07