You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@ignite.apache.org by "Aleksey Zinoviev (JIRA)" <ji...@apache.org> on 2018/08/16 13:05:00 UTC

[jira] [Updated] (IGNITE-9282) [ML] Add Naive Bayes classifier

     [ https://issues.apache.org/jira/browse/IGNITE-9282?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Aleksey Zinoviev updated IGNITE-9282:
-------------------------------------
    Description: 
Naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.

So we want to add this algorithm to Apache Ignite ML module.

Ideally, implementation should support both multinomial naive Bayes and Bernoulli naive Bayes.

Requirements for successful PR:
 # PartitionedDataset usage
 # Trainer-Model paradigm support
 # Tests for Model and for Trainer (and other stuff)
 # Example of usage with small, but famous dataset like IRIS, Titanic or House Prices
 # Javadocs/codestyle according guidelines

 

 

  was:
Naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.

So we want to add this algorithm to Apache Ignite ML module.

Ideally, implementation should support both multinomial naive Bayes and Bernoulli naive Bayes.


> [ML] Add Naive Bayes classifier
> -------------------------------
>
>                 Key: IGNITE-9282
>                 URL: https://issues.apache.org/jira/browse/IGNITE-9282
>             Project: Ignite
>          Issue Type: Sub-task
>          Components: ml
>            Reporter: Aleksey Zinoviev
>            Priority: Major
>
> Naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
> So we want to add this algorithm to Apache Ignite ML module.
> Ideally, implementation should support both multinomial naive Bayes and Bernoulli naive Bayes.
> Requirements for successful PR:
>  # PartitionedDataset usage
>  # Trainer-Model paradigm support
>  # Tests for Model and for Trainer (and other stuff)
>  # Example of usage with small, but famous dataset like IRIS, Titanic or House Prices
>  # Javadocs/codestyle according guidelines
>  
>  



--
This message was sent by Atlassian JIRA
(v7.6.3#76005)