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Posted to commits@spark.apache.org by sr...@apache.org on 2016/09/28 10:12:54 UTC
spark git commit: [SPARK-17017][ML][MLLIB][ML][DOC] Updated the
ml/mllib feature selection docs for ChiSqSelector
Repository: spark
Updated Branches:
refs/heads/master 4a8339568 -> b2a7eedcd
[SPARK-17017][ML][MLLIB][ML][DOC] Updated the ml/mllib feature selection docs for ChiSqSelector
## What changes were proposed in this pull request?
A follow up for #14597 to update feature selection docs about ChiSqSelector.
## How was this patch tested?
Generated html docs. It can be previewed at:
* ml: http://sparkdocs.lins05.pw/spark-17017/ml-features.html#chisqselector
* mllib: http://sparkdocs.lins05.pw/spark-17017/mllib-feature-extraction.html#chisqselector
Author: Shuai Lin <li...@gmail.com>
Closes #15236 from lins05/spark-17017-update-docs-for-chisq-selector-fpr.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/b2a7eedc
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/b2a7eedc
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/b2a7eedc
Branch: refs/heads/master
Commit: b2a7eedcddf0e682ff46afd1b764d0b81ccdf395
Parents: 4a83395
Author: Shuai Lin <li...@gmail.com>
Authored: Wed Sep 28 06:12:48 2016 -0400
Committer: Sean Owen <so...@cloudera.com>
Committed: Wed Sep 28 06:12:48 2016 -0400
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docs/ml-features.md | 14 ++++++++++----
docs/mllib-feature-extraction.md | 14 ++++++++++----
2 files changed, 20 insertions(+), 8 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/b2a7eedc/docs/ml-features.md
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diff --git a/docs/ml-features.md b/docs/ml-features.md
index a39b31c..a7f710f 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1331,10 +1331,16 @@ for more details on the API.
## ChiSqSelector
`ChiSqSelector` stands for Chi-Squared feature selection. It operates on labeled data with
-categorical features. ChiSqSelector orders features based on a
-[Chi-Squared test of independence](https://en.wikipedia.org/wiki/Chi-squared_test)
-from the class, and then filters (selects) the top features which the class label depends on the
-most. This is akin to yielding the features with the most predictive power.
+categorical features. ChiSqSelector uses the
+[Chi-Squared test of independence](https://en.wikipedia.org/wiki/Chi-squared_test) to decide which
+features to choose. It supports three selection methods: `KBest`, `Percentile` and `FPR`:
+
+* `KBest` chooses the `k` top features according to a chi-squared test. This is akin to yielding the features with the most predictive power.
+* `Percentile` is similar to `KBest` but chooses a fraction of all features instead of a fixed number.
+* `FPR` chooses all features whose false positive rate meets some threshold.
+
+By default, the selection method is `KBest`, the default number of top features is 50. User can use
+`setNumTopFeatures`, `setPercentile` and `setAlpha` to set different selection methods.
**Examples**
http://git-wip-us.apache.org/repos/asf/spark/blob/b2a7eedc/docs/mllib-feature-extraction.md
----------------------------------------------------------------------
diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md
index 353d391..87e1e02 100644
--- a/docs/mllib-feature-extraction.md
+++ b/docs/mllib-feature-extraction.md
@@ -225,10 +225,16 @@ features for use in model construction. It reduces the size of the feature space
both speed and statistical learning behavior.
[`ChiSqSelector`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector) implements
-Chi-Squared feature selection. It operates on labeled data with categorical features.
-`ChiSqSelector` orders features based on a Chi-Squared test of independence from the class,
-and then filters (selects) the top features which the class label depends on the most.
-This is akin to yielding the features with the most predictive power.
+Chi-Squared feature selection. It operates on labeled data with categorical features. ChiSqSelector uses the
+[Chi-Squared test of independence](https://en.wikipedia.org/wiki/Chi-squared_test) to decide which
+features to choose. It supports three selection methods: `KBest`, `Percentile` and `FPR`:
+
+* `KBest` chooses the `k` top features according to a chi-squared test. This is akin to yielding the features with the most predictive power.
+* `Percentile` is similar to `KBest` but chooses a fraction of all features instead of a fixed number.
+* `FPR` chooses all features whose false positive rate meets some threshold.
+
+By default, the selection method is `KBest`, the default number of top features is 50. User can use
+`setNumTopFeatures`, `setPercentile` and `setAlpha` to set different selection methods.
The number of features to select can be tuned using a held-out validation set.
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