# sramirez/spark-infotheoretic-feature-selection

<https://repo.masters-masters.com/en/repos/sramirez/spark-infotheoretic-feature-selection>

[Home](https://repo.masters-masters.com/en) › [sramirez](https://repo.masters-masters.com/en/owners/sramirez) › [spark-infotheoretic-feature-selection](https://repo.masters-masters.com/en/repos/sramirez/spark-infotheoretic-feature-selection)

sramirez/spark-infotheoretic-feature-selection has 132 stars on GitHub. Its RepoMasters card: Prototype tier. This package contains a generic implementation…

| Field | Value |
| --- | --- |
| Owner | [sramirez](https://repo.masters-masters.com/en/owners/sramirez) |
| Stars | 132 |
| Forks | 44 |
| Language | [Scala](https://repo.masters-masters.com/en/languages/scala) |
| License | Apache-2.0 |
| Created | 2015 |
| Topics | mrmr, spark, brown, filter, feature-selection |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [sramirez/spark-infotheoretic-feature-selection](https://github.com/sramirez/spark-infotheoretic-feature-selection) |

## Repository

This package contains a generic implementation of greedy Information Theoretic Feature Selection (FS) methods. The implementation is based on the common theoretic framework presented by Gavin Brown. Implementations of mRMR, InfoGain, JMI and other commonly used FS filters are provided.

The tier for repos with 100 to 249 ★. Published odds per card pulled: 70%. Rarity: Common.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.