scalable gradient boosted trees
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csv
doc
eval-libs
.gitignore
LICENSE
OMakefile
OMakeroot
README.md
TODO.md
bV7.ml
bV7.mli
bi_metrics.ml
binary_search.ml
csv2dog.ml
d_feat_map.ml
d_sgbt.ml
d_tree.ml
dense.ml
dense.mli
dog.atd
dog_inspect.ml
dog_io.ml
dog_io.mli
eval.ml
feat.ml
feat_map.ml
feat_map.mli
feat_utils.ml
feat_utils.mli
feature_set.ml
feature_set.mli
gen_c.ml
gen_code.ml
gen_python.ml
heap.ml
heap.mli
huf_hist.ml
huf_hist.mli
iter_literal.ml
lP_tcp.ml
lP_tcp.mli
learn.ml
logistic.ml
logistic.mli
loss.ml
lwt_queue.ml
lwt_queue.mli
main.ml
mod_inspect.ml
model.atd
model_utils.ml
model_utils.mli
proto.atd
rle.ml
rle.mli
rlevec.ml
rlevec.mli
rls1.ml
rls1.mli
roc.ml
sampler.ml
sampler.mli
sgbt.ml
sgbt.mli
square.ml
square.mli
stream_merge.ml
stream_merge.mli
tS.atd
test_bv7.ml
test_huf_hist.ml
test_logistic.ml
test_rle.ml
test_rlevec.ml
test_stream_merge.ml
test_xyz.ml
tree.ml
tree.mli
trie.ml
trie.mli
uInt8Array.ml
uInt8Array.mli
utils.ml
utils.mli
vec.ml
wcli.ml
worker.ml
worker_client.ml

README.md

dawg is a program that build supervised learning models. For documentation on usage, see the wiki.

dawg is written in OCaml. To build it, you will need the following OCaml packages, each of which is available through opam:

  • camlp4
  • omake
  • atdgen
  • mikmatch
  • cmdliner
  • lwt

You can install all the necessary packages with this command: $ opam install atd atdgen cmdliner omake lwt camlp4 pcre mikmatch