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author | Zheng RuiFeng <ruifengz@foxmail.com> | 2016-11-17 13:40:16 +0000 |
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committer | Sean Owen <sowen@cloudera.com> | 2016-11-17 13:40:16 +0000 |
commit | cdaf4ce9fe58c4606be8aa2a5c3756d30545c850 (patch) | |
tree | ccd3b1b51b7c46f01a10c467d306614e685ba4e5 /docs/ml-classification-regression.md | |
parent | de77c67750dc868d75d6af173c3820b75a9fe4b7 (diff) | |
download | spark-cdaf4ce9fe58c4606be8aa2a5c3756d30545c850.tar.gz spark-cdaf4ce9fe58c4606be8aa2a5c3756d30545c850.tar.bz2 spark-cdaf4ce9fe58c4606be8aa2a5c3756d30545c850.zip |
[SPARK-18480][DOCS] Fix wrong links for ML guide docs
## What changes were proposed in this pull request?
1, There are two `[Graph.partitionBy]` in `graphx-programming-guide.md`, the first one had no effert.
2, `DataFrame`, `Transformer`, `Pipeline` and `Parameter` in `ml-pipeline.md` were linked to `ml-guide.html` by mistake.
3, `PythonMLLibAPI` in `mllib-linear-methods.md` was not accessable, because class `PythonMLLibAPI` is private.
4, Other link updates.
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes #15912 from zhengruifeng/md_fix.
Diffstat (limited to 'docs/ml-classification-regression.md')
-rw-r--r-- | docs/ml-classification-regression.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/docs/ml-classification-regression.md b/docs/ml-classification-regression.md index 1aacc3e054..43cc79b9c0 100644 --- a/docs/ml-classification-regression.md +++ b/docs/ml-classification-regression.md @@ -984,7 +984,7 @@ Random forests combine many decision trees in order to reduce the risk of overfi The `spark.ml` implementation supports random forests for binary and multiclass classification and for regression, using both continuous and categorical features. -For more information on the algorithm itself, please see the [`spark.mllib` documentation on random forests](mllib-ensembles.html). +For more information on the algorithm itself, please see the [`spark.mllib` documentation on random forests](mllib-ensembles.html#random-forests). ### Inputs and Outputs @@ -1065,7 +1065,7 @@ GBTs iteratively train decision trees in order to minimize a loss function. The `spark.ml` implementation supports GBTs for binary classification and for regression, using both continuous and categorical features. -For more information on the algorithm itself, please see the [`spark.mllib` documentation on GBTs](mllib-ensembles.html). +For more information on the algorithm itself, please see the [`spark.mllib` documentation on GBTs](mllib-ensembles.html#gradient-boosted-trees-gbts). ### Inputs and Outputs |