package modelkit

  1. Overview
  2. Docs
Portable classical machine learning workflows for OCaml

Install 

dune-project
 Dependency

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

Authors

  1. A
    Asara

Maintainers

  1. A
    Asara developers <devs@asara.io>

Sources

modelkit-0.5.0.tbz
sha256=1fe8fa7c7f904dd098a21a2ca30fd69230750b8cf8aa2ae481b97a16531b47b4
sha512=c946cd1ac014726f4d21791e14d806a205680e6edfa2f80ed8d3680f24f48ca3c5a2f89d5afa68c11eac4053ead448b6381fb3d06dc8ff129097af6c69b262fb

doc/modelkit/Modelkit/Split/index.html

Module Modelkit.Split

A validated train/test selection over one aligned source.

Train and test rows must be non-empty, unique within each partition, disjoint, and aligned to the same source size. materialize explicitly copies both selections into independent aligned datasets; constructing or inspecting a split does not copy dataset buffers.

type t
val create : source_size:int -> train:int array -> test:int array -> (t, Error.t) result
val of_views : train:Row_view.t -> test:Row_view.t -> (t, Error.t) result
val train : t -> Row_view.t
val test : t -> Row_view.t
val materialize : 'kind Dataset.t -> t -> ('kind Dataset.t * 'kind Dataset.t, Error.t) result