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Posted to commits@spark.apache.org by jk...@apache.org on 2016/12/09 20:24:25 UTC
[2/2] spark-website git commit: html changes for mllib site
html changes for mllib site
Project: http://git-wip-us.apache.org/repos/asf/spark-website/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark-website/commit/a82adf04
Tree: http://git-wip-us.apache.org/repos/asf/spark-website/tree/a82adf04
Diff: http://git-wip-us.apache.org/repos/asf/spark-website/diff/a82adf04
Branch: refs/heads/asf-site
Commit: a82adf043744269bfd81d78f2f04e0307fd4626b
Parents: 057cad1
Author: Joseph K. Bradley <jo...@gmail.com>
Authored: Wed Dec 7 12:43:12 2016 -0800
Committer: Joseph K. Bradley <jo...@gmail.com>
Committed: Wed Dec 7 12:43:12 2016 -0800
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site/mllib/index.html | 30 +++++++++++++++++++++---------
1 file changed, 21 insertions(+), 9 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark-website/blob/a82adf04/site/mllib/index.html
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diff --git a/site/mllib/index.html b/site/mllib/index.html
index 11808fb..e29228b 100644
--- a/site/mllib/index.html
+++ b/site/mllib/index.html
@@ -220,7 +220,7 @@
data = spark.read.format(<span class="string">"libsvm"</span>)\<br />
.load(<span class="string">"hdfs://..."</span>)<br />
<br />
- model = <span class="sparkop">KMeans</span>(data, k=10)
+ model = <span class="sparkop">KMeans</span>(k=10).fit(data)
</div>
<div class="caption">Calling MLlib in Python</div>
</div>
@@ -270,25 +270,37 @@
<div class="col-md-4 col-padded">
<h3>Algorithms</h3>
<p>
- MLlib contains many algorithms and utilities, including:
+ MLlib contains many algorithms and utilities.
+ </p>
+ <p>
+ ML algorithms include:
</p>
<ul class="list-narrow">
<li>Classification: logistic regression, naive Bayes,...</li>
- <li>Regression: generalized linear regression, isotonic regression,...</li>
+ <li>Regression: generalized linear regression, survival regression,...</li>
<li>Decision trees, random forests, and gradient-boosted trees</li>
<li>Recommendation: alternating least squares (ALS)</li>
<li>Clustering: K-means, Gaussian mixtures (GMMs),...</li>
<li>Topic modeling: latent Dirichlet allocation (LDA)</li>
+ <li>Frequent itemsets, association rules, and sequential pattern mining</li>
+ </ul>
+ <p>
+ ML workflow utilities include:
+ </p>
+ <ul class="list-narrow">
<li>Feature transformations: standardization, normalization, hashing,...</li>
- <li>Model evaluation and hyper-parameter tuning</li>
<li>ML Pipeline construction</li>
+ <li>Model evaluation and hyper-parameter tuning</li>
<li>ML persistence: saving and loading models and Pipelines</li>
- <li>Survival analysis: accelerated failure time model</li>
- <li>Frequent itemset and sequential pattern mining: FP-growth, association rules, PrefixSpan</li>
- <li>Distributed linear algebra: singular value decomposition (SVD), principal component analysis (PCA),...</li>
+ </ul>
+ <p>
+ Other utilities include:
+ </p>
+ <ul class="list-narrow">
+ <li>Distributed linear algebra: SVD, PCA,...</li>
<li>Statistics: summary statistics, hypothesis testing,...</li>
</ul>
- <p>Refer to the <a href="/docs/latest/mllib-guide.html">MLlib guide</a> for usage examples.</p>
+ <p>Refer to the <a href="/docs/latest/ml-guide.html">MLlib guide</a> for usage examples.</p>
</div>
<div class="col-md-4 col-padded">
@@ -315,7 +327,7 @@
</p>
<ul class="list-narrow">
<li><a href="/downloads.html">Download Spark</a>. MLlib is included as a module.</li>
- <li>Read the <a href="/docs/latest/mllib-guide.html">MLlib guide</a>, which includes
+ <li>Read the <a href="/docs/latest/ml-guide.html">MLlib guide</a>, which includes
various usage examples.</li>
<li>Learn how to <a href="/docs/latest/#launching-on-a-cluster">deploy</a> Spark on a cluster
if you'd like to run in distributed mode. You can also run locally on a multicore machine
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