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| author | Xiangrui Meng <meng@apache.org> | 2015-04-29 02:52:51 +0000 |
|---|---|---|
| committer | Xiangrui Meng <meng@apache.org> | 2015-04-29 02:52:51 +0000 |
| commit | 60d4a0f5a0f2ec6f038b11ebe7341ba763be84af (patch) | |
| tree | 6945bd3dcfe98367391a99c1af6411b89e7d82e5 | |
| parent | 8f96a0fe30232a631ff93fc37743fee94eb27a9f (diff) | |
| download | spark-website-60d4a0f5a0f2ec6f038b11ebe7341ba763be84af.tar.gz spark-website-60d4a0f5a0f2ec6f038b11ebe7341ba763be84af.tar.bz2 spark-website-60d4a0f5a0f2ec6f038b11ebe7341ba763be84af.zip | |
update mllib algorithms to 1.3
| -rw-r--r-- | mllib/index.md | 8 | ||||
| -rw-r--r-- | site/mllib/index.html | 8 |
2 files changed, 12 insertions, 4 deletions
diff --git a/mllib/index.md b/mllib/index.md index bdd133324..da0288db1 100644 --- a/mllib/index.md +++ b/mllib/index.md @@ -83,16 +83,20 @@ subproject: MLlib <div class="col-md-4 col-padded"> <h3>Algorithms</h3> <p> - MLlib 1.1 contains the following algorithms: + MLlib 1.3 contains the following algorithms: </p> <ul class="list-narrow"> <li>linear SVM and logistic regression</li> <li>classification and regression tree</li> - <li>k-means clustering</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>isotonic regression</li> <li>multinomial naive Bayes</li> + <li>frequent itemset mining via FP-growth</li> <li>basic statistics</li> <li>feature transformations</li> </ul> diff --git a/site/mllib/index.html b/site/mllib/index.html index 70165d0f7..86dd59fcb 100644 --- a/site/mllib/index.html +++ b/site/mllib/index.html @@ -252,16 +252,20 @@ <div class="col-md-4 col-padded"> <h3>Algorithms</h3> <p> - MLlib 1.1 contains the following algorithms: + MLlib 1.3 contains the following algorithms: </p> <ul class="list-narrow"> <li>linear SVM and logistic regression</li> <li>classification and regression tree</li> - <li>k-means clustering</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>isotonic regression</li> <li>multinomial naive Bayes</li> + <li>frequent itemset mining via FP-growth</li> <li>basic statistics</li> <li>feature transformations</li> </ul> |