[
https://issues.apache.org/jira/browse/SPARK-4894?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14277631#comment-14277631
]
RJ Nowling commented on SPARK-4894:
-----------------------------------
Thanks [~lmcguire]! I'll wait until next week in case you have time to put a
patch together.
In the mean time, here were my thoughts for changes:
1. Add an optional `model` variable to the `NaiveBayes` object and class and
`NaiveBayesModel`. It would be a string with a default value of `Multinomial`.
For Bernoulli, we can use `Bernoulli`.
2. In `NaiveBayesModel.predict`, we should compute and store `brzPi + brzTheta
* testData.toBreeze`. If `testData(i)` is 0, then `brzTheta *
testData.toBreeze` will be 0. If Bernoulli is enabled, we add `log(1 -
exp(brzTheta)) * (1 - testData.toBreeze)` to account for the probabilities for
the 0-valued features. (Breeze may not allow adding/subtracting scalars and
vectors/matrices.)
In the current model, no term is added for rows of `testData` that have 0
entries. In the Bernoulli model, we would be adding a separate term for
0-valued features.
Here is the sklearn source for comparison:
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/naive_bayes.py
Note that sklearn adds the neg prob to all features and subtracts it from
features with 1-values.
[~mengxr], [~josephkb] Any thoughts or comments?
> Add Bernoulli-variant of Naive Bayes
> ------------------------------------
>
> Key: SPARK-4894
> URL: https://issues.apache.org/jira/browse/SPARK-4894
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 1.2.0
> Reporter: RJ Nowling
> Assignee: RJ Nowling
>
> MLlib only supports the multinomial-variant of Naive Bayes. The Bernoulli
> version of Naive Bayes is more useful for situations where the features are
> binary values.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
