orf.opam

opam-version: "2.0"
authors: "Francois Berenger"
maintainer: "unixjunkie@sdf.org"
homepage: "https://github.com/UnixJunkie/orf"
bug-reports: "https://github.com/UnixJunkie/orf/issues"
dev-repo: "git+https://github.com/UnixJunkie/orf.git"
license: "LGPL-2.1-or-later WITH OCaml-LGPL-linking-exception"
build: ["dune" "build" "-p" name "-j" jobs]
depends: [
  "batteries" {>= "3.2.0"}
  "cpm" {>= "6.0.0"}
  "dolog" {>= "4.0.0"}
  "dune" {>= "2.8"}
  "minicli"
  "ocaml"
  "parany" {>= "11.0.0"}
  "line_oriented"
]
depopts: [
  "conf-gnuplot"
]
synopsis: "OCaml Random Forests"
description:"""
Random Forests (RFs) can do classification or regression modeling.

Random Forests 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) data-set.

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 only consider a sparse vector
of integers as features. i.e. categorical variables will need to be
one-hot-encoded.
For classification, the dependent variable must be an integer
(encoding a class label).
For regression, the dependent variable must be a float.

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."""
# url {
#   src: "https://github.com/UnixJunkie/orf/archive/refs/tags/vXXX.tar.gz"
#   checksum: "md5=YYY"
# }