Papers

Mondrian Forests: Efficient Online Random Forests

• arxiv: http://arxiv.org/abs/1406.2673
• github: https://github.com/balajiln/mondrianforest
• video: http://videolectures.net/sahd2014_teh_mondrian_forests/

Food-101 – Mining Discriminative Components with Random Forests

• homepage: http://www.vision.ee.ethz.ch/datasets/food-101/

Bernoulli Random Forests: Closing the Gap between Theoretical Consistency and Empirical Soundness (IJCAI 2016)

• paper: http://www.ijcai.org/Proceedings/16/Papers/309.pdf

TensorForest: Scalable Random Forests on TensorFlow

• intro: Google Inc. NIPS 2016
• paper: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxtbHN5c25pcHMyMDE2fGd4OjFlNTRiOWU2OGM2YzA4MjE
• github: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/tensor_forest

ggRandomForests: Exploring Random Forest Survival

• intro: ggRandomForests: Visually Exploring Random Forests
• arxiv: https://arxiv.org/abs/1612.08974
• package(R): https://cran.r-project.org/web/packages/ggRandomForests/index.html
• github: https://github.com/ehrlinger/ggRandomForests

Resources

AWESOME-RANDOM-FOREST - a curated list of resources regarding random forest

• blog: http://jiwonkim.org/awesome-random-forest/
• github: https://github.com/kjw0612/awesome-random-forest

Blogs

The Unreasonable Effectiveness of Random Forests

• blog: https://medium.com/rants-on-machine-learning/the-unreasonable-effectiveness-of-random-forests-f33c3ce28883
• mirror: http://pan.baidu.com/s/1gd6dsMR

Random forest interpretation with scikit-learn

• blog: http://blog.datadive.net/random-forest-interpretation-with-scikit-learn/
• github: https://github.com/andosa/treeinterpreter

Machine learning - Random forests (by Nando de Freitas)

• youtube: https://www.youtube.com/watch?v=3kYujfDgmNk&hd=1
• lectures: http://www.cs.ubc.ca/~nando/540-2013/lectures.html

A tour of random forests

https://codewords.recurse.com/issues/seven/a-tour-of-random-forests

Projects

Random Forest demo in Javascript

• demo: http://cs.stanford.edu/people/karpathy/svmjs/demo/demoforest.html
• github: https://github.com/karpathy/forestjs

ParallelForest: Random Forest Classification with Parallel Computing

• intro: R package implementing random forest classification using parallel computing, built with Fortran and OpenMP in the backend.
• project page: https://cran.r-project.org/web/packages/ParallelForest/vignettes/ParallelForest-intro.html
• github: https://github.com/bert9bert/ParallelForest