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 (2)

Authors

  1. A
    Asara

Maintainers

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

Sources

modelkit-0.3.2.tbz
sha256=a0335f799da12302a0568320b5f5c4c26c531a336740fea07f326cb2729ae8ac
sha512=bc692df9c0a868f99c14159c03c0c975d6fd51d6283a0e8491197abde8cebcd04f26ba990b66dc3ac711ab01bc8f7c2a4279fdf5b30a64ded5f622e0b2bcc630

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