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author | Timothy Hunter <timhunter@databricks.com> | 2015-12-10 12:50:46 -0800 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2015-12-10 12:50:46 -0800 |
commit | 2ecbe02d5b28ee562d10c1735244b90a08532c9e (patch) | |
tree | c589a01a2900513aa1b277303ed7cdffc1961ba4 /docs/ml-guide.md | |
parent | ec5f9ed5de2218938dba52152475daafd4dc4786 (diff) | |
download | spark-2ecbe02d5b28ee562d10c1735244b90a08532c9e.tar.gz spark-2ecbe02d5b28ee562d10c1735244b90a08532c9e.tar.bz2 spark-2ecbe02d5b28ee562d10c1735244b90a08532c9e.zip |
[SPARK-12212][ML][DOC] Clarifies the difference between spark.ml, spark.mllib and mllib in the documentation.
Replaces a number of occurences of `MLlib` in the documentation that were meant to refer to the `spark.mllib` package instead. It should clarify for new users the difference between `spark.mllib` (the package) and MLlib (the umbrella project for ML in spark).
It also removes some files that I forgot to delete with #10207
Author: Timothy Hunter <timhunter@databricks.com>
Closes #10234 from thunterdb/12212.
Diffstat (limited to 'docs/ml-guide.md')
-rw-r--r-- | docs/ml-guide.md | 19 |
1 files changed, 3 insertions, 16 deletions
diff --git a/docs/ml-guide.md b/docs/ml-guide.md index 6f35b30c3d..5c96c2b7d5 100644 --- a/docs/ml-guide.md +++ b/docs/ml-guide.md @@ -1,8 +1,10 @@ --- layout: global -title: Spark ML Programming Guide +title: "Overview: estimators, transformers and pipelines - spark.ml" +displayTitle: "Overview: estimators, transformers and pipelines - spark.ml" --- + `\[ \newcommand{\R}{\mathbb{R}} \newcommand{\E}{\mathbb{E}} @@ -32,21 +34,6 @@ See the [algorithm guides](#algorithm-guides) section below for guides on sub-pa * This will become a table of contents (this text will be scraped). {:toc} -# Algorithm guides - -We provide several algorithm guides specific to the Pipelines API. -Several of these algorithms, such as certain feature transformers, are not in the `spark.mllib` API. -Also, some algorithms have additional capabilities in the `spark.ml` API; e.g., random forests -provide class probabilities, and linear models provide model summaries. - -* [Feature extraction, transformation, and selection](ml-features.html) -* [Clustering](ml-clustering.html) -* [Decision Trees for classification and regression](ml-decision-tree.html) -* [Ensembles](ml-ensembles.html) -* [Linear methods with elastic net regularization](ml-linear-methods.html) -* [Multilayer perceptron classifier](ml-ann.html) -* [Survival Regression](ml-survival-regression.html) - # Main concepts in Pipelines |