GitHub user mpjlu opened a pull request:
https://github.com/apache/spark/pull/15212
[MLLIB][ML]add feature selector method based on: False Discovery Rate (FDR)
and Family wise error rate (FWE)
## What changes were proposed in this pull request?
Univariate feature selection works by selecting the best features based on
univariate statistical tests.
FDR and FWE are a popular univariate statistical test for feature
selection.
In 2005, the Benjamini and Hochberg paper on FDR was identified as one of
the 25 most-cited statistical papers. The FDR uses the Benjamini-Hochberg
procedure in this PR. https://en.wikipedia.org/wiki/False_discovery_rate.
In statistics, FWE is the probability of making one or more false
discoveries, or type I errors, among all the hypotheses when performing
multiple hypotheses tests.
https://en.wikipedia.org/wiki/Family-wise_error_rate
We add FDR and FWE methods for ChiSqSelector in this PR, like it is
implemented in scikit-learn.
http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection
## How was this patch tested?
ut will be added soon
(Please explain how this patch was tested. E.g. unit tests, integration
tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise,
remove this)
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/mpjlu/spark fdr_fwe
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/15212.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #15212
----
commit 2c071791b2c6fd7d388343ac95783c32ffdae529
Author: Peng, Meng <[email protected]>
Date: 2016-09-23T07:27:19Z
add feature selector method: FDR and FWE
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