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Racket Machine Learning — Core

Racket Machine Learning — Core

Simon Johnston <johnstonskj@gmail.com>

This Package is part of an expected set of packages implementing machine learning capabilities for Racket. The core of this package is the management of data sets, individuals, and results and the classification of individuals against known data sets.

This package does not assume anything about specific capabilities, and uses an expansive notion of machine learning that should cover statistical inferencing, tree and decision matrix models, as well as deep leaning approaches.

    1 Module rml/data

      1.1 Types and Predicates

      1.2 Construction

      1.3 Accessors

      1.4 Transformations

      1.5 Snapshots

    2 Module rml/individual

      2.1 Types and Predicates

      2.2 Construction

      2.3 Partition Generator

    3 Module rml/classify

      3.1 Partitioned Classification

    4 Module rml/statistics

    5 Module rml/gini

    6 Module rml/results

      6.1 Types and Predicates

      6.2 Construction

      6.3 Recording Results

    7 Module rml/not-implemented