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author | Kousuke Saruta <sarutak@oss.nttdata.co.jp> | 2014-10-23 09:19:32 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2014-10-23 09:19:32 -0700 |
commit | f799700eec4a5e33db9b2d6a4bee60a50fd5a099 (patch) | |
tree | 2fdb00c74e6dc8ef637f37e9f094e7547d3c0b31 /docs | |
parent | d6a302539213e8cdb51ca14b1769aeb33f9f435f (diff) | |
download | spark-f799700eec4a5e33db9b2d6a4bee60a50fd5a099.tar.gz spark-f799700eec4a5e33db9b2d6a4bee60a50fd5a099.tar.bz2 spark-f799700eec4a5e33db9b2d6a4bee60a50fd5a099.zip |
[SPARK-4055][MLlib] Inconsistent spelling 'MLlib' and 'MLLib'
Thare are some inconsistent spellings 'MLlib' and 'MLLib' in some documents and source codes.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes #2903 from sarutak/SPARK-4055 and squashes the following commits:
b031640 [Kousuke Saruta] Fixed inconsistent spelling "MLlib and MLLib"
Diffstat (limited to 'docs')
-rw-r--r-- | docs/mllib-feature-extraction.md | 2 | ||||
-rw-r--r-- | docs/mllib-statistics.md | 2 |
2 files changed, 2 insertions, 2 deletions
diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md index 1511ae6dda..1162241449 100644 --- a/docs/mllib-feature-extraction.md +++ b/docs/mllib-feature-extraction.md @@ -83,7 +83,7 @@ val idf = new IDF().fit(tf) val tfidf: RDD[Vector] = idf.transform(tf) {% endhighlight %} -MLLib's IDF implementation provides an option for ignoring terms which occur in less than a +MLlib's IDF implementation provides an option for ignoring terms which occur in less than a minimum number of documents. In such cases, the IDF for these terms is set to 0. This feature can be used by passing the `minDocFreq` value to the IDF constructor. diff --git a/docs/mllib-statistics.md b/docs/mllib-statistics.md index c463241399..10a5131c07 100644 --- a/docs/mllib-statistics.md +++ b/docs/mllib-statistics.md @@ -197,7 +197,7 @@ print Statistics.corr(data, method="pearson") ## Stratified sampling -Unlike the other statistics functions, which reside in MLLib, stratified sampling methods, +Unlike the other statistics functions, which reside in MLlib, stratified sampling methods, `sampleByKey` and `sampleByKeyExact`, can be performed on RDD's of key-value pairs. For stratified sampling, the keys can be thought of as a label and the value as a specific attribute. For example the key can be man or woman, or document ids, and the respective values can be the list of ages |