package modelkit

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Portable classical machine learning workflows for OCaml

Install

dune-project

Dependency

github.com Readme Changelog Edit opam file Versions (4)

Authors

A

Asara

Maintainers

A

Asara developers <devs@asara.io>

Sources

modelkit-0.5.0.tbz

sha256=1fe8fa7c7f904dd098a21a2ca30fd69230750b8cf8aa2ae481b97a16531b47b4

sha512=c946cd1ac014726f4d21791e14d806a205680e6edfa2f80ed8d3680f24f48ca3c5a2f89d5afa68c11eac4053ead448b6381fb3d06dc8ff129097af6c69b262fb

doc/modelkit/Modelkit/Train_test_split/index.html

Module `Modelkit.Train_test_split`

Materialized train/test datasets with all row-aligned fields selected together, preserving feature schema and finiteness policy.

val split : 
  ?train_size:Split_size.t ->
  ?test_size:Split_size.t ->
  ?shuffle:bool ->
  ?stratify:Target.classification Target.t ->
  rng:Rng.t ->
  'kind Dataset.t ->
  unit ->
  ('kind Dataset.t * 'kind Dataset.t, Error.t) result

Defaults to shuffling and a 25% test fraction, like Holdout. Optional classification labels must match the source row count and require shuffle=true; stratification uses Stratified_shuffle_split's allocation rules. Dataset groups are copied, not kept exclusive. All fields follow the exact selected order; source data is immutable. Materialization can fail if a selected sample-weight partition has zero total weight. For row views instead of copies, use the splitter modules and Split.of_views.