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| author | Timothy Hunter <timhunter@databricks.com> | 2015-12-10 12:50:46 -0800 |
|---|---|---|
| committer | Joseph K. Bradley <joseph@databricks.com> | 2015-12-10 12:50:46 -0800 |
| commit | 2ecbe02d5b28ee562d10c1735244b90a08532c9e (patch) | |
| tree | c589a01a2900513aa1b277303ed7cdffc1961ba4 /docs/mllib-decision-tree.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/mllib-decision-tree.md')
| -rw-r--r-- | docs/mllib-decision-tree.md | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/docs/mllib-decision-tree.md b/docs/mllib-decision-tree.md index 77ce34e91a..a8612b6c84 100644 --- a/docs/mllib-decision-tree.md +++ b/docs/mllib-decision-tree.md @@ -1,7 +1,7 @@ --- layout: global -title: Decision Trees - MLlib -displayTitle: <a href="mllib-guide.html">MLlib</a> - Decision Trees +title: Decision Trees - spark.mllib +displayTitle: Decision Trees - spark.mllib --- * Table of contents @@ -15,7 +15,7 @@ feature scaling, and are able to capture non-linearities and feature interaction algorithms such as random forests and boosting are among the top performers for classification and regression tasks. -MLlib supports decision trees for binary and multiclass classification and for regression, +`spark.mllib` supports decision trees for binary and multiclass classification and for regression, using both continuous and categorical features. The implementation partitions data by rows, allowing distributed training with millions of instances. |