Author: meng
Date: Wed Aug 19 19:11:08 2015
New Revision: 1696648
URL: http://svn.apache.org/r1696648
Log:
update MLlib page for 1.5
Modified:
spark/mllib/index.md
spark/site/mllib/index.html
Modified: spark/mllib/index.md
URL:
http://svn.apache.org/viewvc/spark/mllib/index.md?rev=1696648&r1=1696647&r2=1696648&view=diff
==============================================================================
--- spark/mllib/index.md (original)
+++ spark/mllib/index.md Wed Aug 19 19:11:08 2015
@@ -14,7 +14,7 @@ subproject: MLlib
<div class="col-md-7 col-sm-7">
<h2>Ease of Use</h2>
<p class="lead">
- Usable in Java, Scala and Python.
+ Usable in Java, Scala, Python, and SparkR.
</p>
<p>
MLlib fits into <a href="{{site.url}}">Spark</a>'s
@@ -83,22 +83,25 @@ subproject: MLlib
<div class="col-md-4 col-padded">
<h3>Algorithms</h3>
<p>
- MLlib 1.3 contains the following algorithms:
+ MLlib contains the following algorithms and utilities:
</p>
<ul class="list-narrow">
- <li>linear SVM and logistic regression</li>
+ <li>logistic regression and linear support vector machine (SVM)</li>
<li>classification and regression tree</li>
<li>random forest and gradient-boosted trees</li>
- <li>recommendation via alternating least squares</li>
- <li>clustering via k-means, Gaussian mixtures, and power iteration
clustering</li>
- <li>topic modeling via latent Dirichlet allocation</li>
- <li>singular value decomposition</li>
- <li>linear regression with L<sub>1</sub>- and
L<sub>2</sub>-regularization</li>
+ <li>recommendation via alternating least squares (ALS)</li>
+ <li>clustering via k-means, Gaussian mixtures (GMM), and power iteration
clustering</li>
+ <li>topic modeling via latent Dirichlet allocation (LDA)</li>
+ <li>singular value decomposition (SVD) and QR decomposition</li>
+ <li>principal component analysis (PCA)</li>
+ <li>linear regression with L<sub>1</sub>, L<sub>2</sub>, and elastic-net
regularization</li>
<li>isotonic regression</li>
- <li>multinomial naive Bayes</li>
- <li>frequent itemset mining via FP-growth</li>
- <li>basic statistics</li>
+ <li>multinomial/binomial naive Bayes</li>
+ <li>frequent itemset mining via FP-growth and association rules</li>
+ <li>sequential pattern mining via PrefixSpan</li>
+ <li>summary statistics and hypothesis testing</li>
<li>feature transformations</li>
+ <li>model evaluation and hyper-parameter tuning</li>
</ul>
<p>Refer to the <a href="{{site.url}}docs/latest/mllib-guide.html">MLlib
guide</a> for usage examples.</p>
</div>
Modified: spark/site/mllib/index.html
URL:
http://svn.apache.org/viewvc/spark/site/mllib/index.html?rev=1696648&r1=1696647&r2=1696648&view=diff
==============================================================================
--- spark/site/mllib/index.html (original)
+++ spark/site/mllib/index.html Wed Aug 19 19:11:08 2015
@@ -178,7 +178,7 @@
<div class="col-md-7 col-sm-7">
<h2>Ease of Use</h2>
<p class="lead">
- Usable in Java, Scala and Python.
+ Usable in Java, Scala, Python, and SparkR.
</p>
<p>
MLlib fits into <a href="/">Spark</a>'s
@@ -250,22 +250,25 @@
<div class="col-md-4 col-padded">
<h3>Algorithms</h3>
<p>
- MLlib 1.3 contains the following algorithms:
+ MLlib contains the following algorithms and utilities:
</p>
<ul class="list-narrow">
- <li>linear SVM and logistic regression</li>
+ <li>logistic regression and linear support vector machine (SVM)</li>
<li>classification and regression tree</li>
<li>random forest and gradient-boosted trees</li>
- <li>recommendation via alternating least squares</li>
- <li>clustering via k-means, Gaussian mixtures, and power iteration
clustering</li>
- <li>topic modeling via latent Dirichlet allocation</li>
- <li>singular value decomposition</li>
- <li>linear regression with L<sub>1</sub>- and
L<sub>2</sub>-regularization</li>
+ <li>recommendation via alternating least squares (ALS)</li>
+ <li>clustering via k-means, Gaussian mixtures (GMM), and power iteration
clustering</li>
+ <li>topic modeling via latent Dirichlet allocation (LDA)</li>
+ <li>singular value decomposition (SVD) and QR decomposition</li>
+ <li>principal component analysis (PCA)</li>
+ <li>linear regression with L<sub>1</sub>, L<sub>2</sub>, and elastic-net
regularization</li>
<li>isotonic regression</li>
- <li>multinomial naive Bayes</li>
- <li>frequent itemset mining via FP-growth</li>
- <li>basic statistics</li>
+ <li>multinomial/binomial naive Bayes</li>
+ <li>frequent itemset mining via FP-growth and association rules</li>
+ <li>sequential pattern mining via PrefixSpan</li>
+ <li>summary statistics and hypothesis testing</li>
<li>feature transformations</li>
+ <li>model evaluation and hyper-parameter tuning</li>
</ul>
<p>Refer to the <a href="/docs/latest/mllib-guide.html">MLlib guide</a>
for usage examples.</p>
</div>
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