mlr is our R package to make machine learning in R easy for you and for us. Trying out and comparing different machine learning methods in R was always a tedious task as you had to check every documentation to see how each function is called. And not even the output is directly comparable. Worry no more! mlr has you covered: cross-validation, variable selection, tuning and many more can be easily done with mlr and a standardized interface!
This blog is the platform for some R developers, statisticians and data scientists to post hopefully interesting bits of knowledge about machine learning. In one way or another we all contributed to mlr and want to present you some highlights, hints and insights of mlr. We try to post neat stuff we learned and we want to promote on an irregular basis.
All mlr developers are listed here.
I am PostDoc at the Statistics Department at the University Dortmund. I work on parallelization, hyperparameter optimization and survival analysis. More information can be found at my website or GitHub.
Hi, I am a PhD candidate working full time at TU Dortmund for the SFB 876. There we focus on Resource-Constraint Data Analysis. My research interests are hyperparameter optimization, model based optimization (mlrMBO) and both in combination. For the latter we develop new algorithms focused especially on resource awareness. You can check out my contributions on my github and check out my irregularly updated blog.
I am professor of computational statistics at the LMU Munich and created mlr a long time ago. Go to my personal page for any further info on me.
I am a PhD Student in statistics and machine learning at IBE, LMU Munich. My research interests cover tree-based methods and ensemble techniques, hyperparameters of ML algorithms, benchmark experiments and multilabel classification. More information about me and some other blog posts can be found at my blog.
I am a graduate student at Pennsylvania State University where I work on methods for interpreting statistical learning methods, dependent data, latent variable models for networks, and applications of these methods in the social sciences. More information is on my website.
I am a graduate student at Georgia Tech with industry experience in machine learning. In addition to my research and open-source work, I am also passionate about training and education. Go to my personal page to learn more about me.
I am a PhD candidate in Biostatistics at the Universtiy of Zurich (EBPI). My research is on model-based recursive partitioning in the context of clinical trials. More information on my research is on my website. I organize the Zurich R User meetup and write a blog.
I am a PhD student in the working group computational statistics at the Ludwig-Maximilians-University Munich. Most on my work is on hyperparameter tuning, variable selection and gradient boosting. Further information and contact info is at my university website.
I am a PhD student in the working group computational statistics at the Ludwig-Maximilians-University Munich. My research interests focus on hyperparameter tuning, variable selection, visualizing the predictive performance and gaining insights from machine learning algorithms. Further information and contact info is at my university website.
I am a PhD student in the working group computational statistics at the Ludwig-Maximilians-University Munich. My research interests are in the area of machine learning, especially reinforcement learning in the context of Human-Machine-Interaction. You can find me on my university website.