https://github.com/unixjunkie/orf

OCaml Random Forests
https://github.com/unixjunkie/orf

bagging bootstrap-aggregation classification machine-learning ocaml-library out-of-bag random-forests regression statistical-modeling

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OCaml Random Forests

• Host: GitHub
• URL: https://github.com/unixjunkie/orf
• Owner: UnixJunkie
• License: other
• Created: 2021-06-08T03:37:04.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2023-01-10T00:23:28.000Z (almost 2 years ago)
• Last Synced: 2023-03-22T11:08:13.640Z (over 1 year ago)
• Topics: bagging, bootstrap-aggregation, classification, machine-learning, ocaml-library, out-of-bag, random-forests, regression, statistical-modeling
• Language: OCaml
• Homepage:
• Size: 1.11 MB
• Stars: 8
• Watchers: 2
• Forks: 1
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# ORF: OCaml Random Forests

Random Forests (RFs) are one of the workhorse of modern machine learning.

Especially, they cannot over-fit to the training set, are

fast to train, predict fast, parallelize well and give you a reasonable model

even without optimizing the model's default hyper-parameters.

In other words, it is hard to shoot yourself in the foot while

training or exploiting a Random Forests model.

In comparison, with deep neural networks

it is very easy to shoot yourself in the foot.

Using out of bag (OOB) samples, you can even get an idea

of a RFs performance, without the need for a held out

(test) dataset.

Their only drawback is that RFs, being an ensemble model,

cannot predict values which are outside of the training set

range of values (this _is_ a serious limitation in case you

are trying to optimize or minimize something in order to discover

outliers, compared to your training set samples).

For the moment, this implementation will only consider a sparse vector of

integers as features. i.e. categorical variables will need to be

one-hot-encoded.

# Bibliography

Breiman, Leo. (1996). "Bagging Predictors". Machine learning, 24(2), 123-140.

Breiman, Leo. (2001). "Random Forests". Machine learning, 45(1), 5-32.

Geurts, P., Ernst, D., & Wehenkel, L. (2006). "Extremely Randomized Trees".

Machine learning, 63(1), 3-42.