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| author | Ram Sriharsha <rsriharsha@hw11853.local> | 2015-05-22 13:18:08 -0700 |
|---|---|---|
| committer | Joseph K. Bradley <joseph@databricks.com> | 2015-05-22 13:18:08 -0700 |
| commit | 509d55ab416359fab0525189458e2ea96379cf14 (patch) | |
| tree | 5c3562735a440950537ce1ba7d37e656aad107c9 /docs/ml-guide.md | |
| parent | c63036cd475cfc26093c296ca1be13802c51093a (diff) | |
| download | spark-509d55ab416359fab0525189458e2ea96379cf14.tar.gz spark-509d55ab416359fab0525189458e2ea96379cf14.tar.bz2 spark-509d55ab416359fab0525189458e2ea96379cf14.zip | |
[SPARK-7574] [ML] [DOC] User guide for OneVsRest
Including Iris Dataset (after shuffling and relabeling 3 -> 0 to confirm to 0 -> numClasses-1 labeling). Could not find an existing dataset in data/mllib for multiclass classification.
Author: Ram Sriharsha <rsriharsha@hw11853.local>
Closes #6296 from harsha2010/SPARK-7574 and squashes the following commits:
645427c [Ram Sriharsha] cleanup
46c41b1 [Ram Sriharsha] cleanup
2f76295 [Ram Sriharsha] Code Review Fixes
ebdf103 [Ram Sriharsha] Java Example
c026613 [Ram Sriharsha] Code Review fixes
4b7d1a6 [Ram Sriharsha] minor cleanup
13bed9c [Ram Sriharsha] add wikipedia link
bb9dbfa [Ram Sriharsha] Clean up naming
6f90db1 [Ram Sriharsha] [SPARK-7574][ml][doc] User guide for OneVsRest
Diffstat (limited to 'docs/ml-guide.md')
| -rw-r--r-- | docs/ml-guide.md | 3 |
1 files changed, 2 insertions, 1 deletions
diff --git a/docs/ml-guide.md b/docs/ml-guide.md index cac705683c..c5f50ed799 100644 --- a/docs/ml-guide.md +++ b/docs/ml-guide.md @@ -150,11 +150,12 @@ This is useful if there are two algorithms with the `maxIter` parameter in a `Pi # Algorithm Guides -There are now several algorithms in the Pipelines API which are not in the lower-level MLlib API, so we link to documentation for them here. These algorithms are mostly feature transformers, which fit naturally into the `Transformer` abstraction in Pipelines. +There are now several algorithms in the Pipelines API which are not in the lower-level MLlib API, so we link to documentation for them here. These algorithms are mostly feature transformers, which fit naturally into the `Transformer` abstraction in Pipelines, and ensembles, which fit naturally into the `Estimator` abstraction in the Pipelines. **Pipelines API Algorithm Guides** * [Feature Extraction, Transformation, and Selection](ml-features.html) +* [Ensembles](ml-ensembles.html) # Code Examples |