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author | Dongjoon Hyun <dongjoon@apache.org> | 2016-06-11 12:55:38 +0100 |
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committer | Sean Owen <sowen@cloudera.com> | 2016-06-11 12:55:38 +0100 |
commit | ad102af169c7344b30d3b84aa16452fcdc22542c (patch) | |
tree | 3ddc38bba4e271d6e361c7a880d12c030a76a532 /docs/mllib-decision-tree.md | |
parent | 3761330dd0151d7369d7fba4d4c344e9863990ef (diff) | |
download | spark-ad102af169c7344b30d3b84aa16452fcdc22542c.tar.gz spark-ad102af169c7344b30d3b84aa16452fcdc22542c.tar.bz2 spark-ad102af169c7344b30d3b84aa16452fcdc22542c.zip |
[SPARK-15883][MLLIB][DOCS] Fix broken links in mllib documents
## What changes were proposed in this pull request?
This issue fixes all broken links on Spark 2.0 preview MLLib documents. Also, this contains some editorial change.
**Fix broken links**
* mllib-data-types.md
* mllib-decision-tree.md
* mllib-ensembles.md
* mllib-feature-extraction.md
* mllib-pmml-model-export.md
* mllib-statistics.md
**Fix malformed section header and scala coding style**
* mllib-linear-methods.md
**Replace indirect forward links with direct one**
* ml-classification-regression.md
## How was this patch tested?
Manual tests (with `cd docs; jekyll build`.)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #13608 from dongjoon-hyun/SPARK-15883.
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 9af48357b3..11f5de1fc9 100644 --- a/docs/mllib-decision-tree.md +++ b/docs/mllib-decision-tree.md @@ -136,7 +136,7 @@ When tuning these parameters, be careful to validate on held-out test data to av * **`maxDepth`**: Maximum depth of a tree. Deeper trees are more expressive (potentially allowing higher accuracy), but they are also more costly to train and are more likely to overfit. -* **`minInstancesPerNode`**: For a node to be split further, each of its children must receive at least this number of training instances. This is commonly used with [RandomForest](api/scala/index.html#org.apache.spark.mllib.tree.RandomForest) since those are often trained deeper than individual trees. +* **`minInstancesPerNode`**: For a node to be split further, each of its children must receive at least this number of training instances. This is commonly used with [RandomForest](api/scala/index.html#org.apache.spark.mllib.tree.RandomForest$) since those are often trained deeper than individual trees. * **`minInfoGain`**: For a node to be split further, the split must improve at least this much (in terms of information gain). @@ -152,13 +152,13 @@ These parameters may be tuned. Be careful to validate on held-out test data whe * The default value is conservatively chosen to be 256 MB to allow the decision algorithm to work in most scenarios. Increasing `maxMemoryInMB` can lead to faster training (if the memory is available) by allowing fewer passes over the data. However, there may be decreasing returns as `maxMemoryInMB` grows since the amount of communication on each iteration can be proportional to `maxMemoryInMB`. * *Implementation details*: For faster processing, the decision tree algorithm collects statistics about groups of nodes to split (rather than 1 node at a time). The number of nodes which can be handled in one group is determined by the memory requirements (which vary per features). The `maxMemoryInMB` parameter specifies the memory limit in terms of megabytes which each worker can use for these statistics. -* **`subsamplingRate`**: Fraction of the training data used for learning the decision tree. This parameter is most relevant for training ensembles of trees (using [`RandomForest`](api/scala/index.html#org.apache.spark.mllib.tree.RandomForest) and [`GradientBoostedTrees`](api/scala/index.html#org.apache.spark.mllib.tree.GradientBoostedTrees)), where it can be useful to subsample the original data. For training a single decision tree, this parameter is less useful since the number of training instances is generally not the main constraint. +* **`subsamplingRate`**: Fraction of the training data used for learning the decision tree. This parameter is most relevant for training ensembles of trees (using [`RandomForest`](api/scala/index.html#org.apache.spark.mllib.tree.RandomForest$) and [`GradientBoostedTrees`](api/scala/index.html#org.apache.spark.mllib.tree.GradientBoostedTrees)), where it can be useful to subsample the original data. For training a single decision tree, this parameter is less useful since the number of training instances is generally not the main constraint. * **`impurity`**: Impurity measure (discussed above) used to choose between candidate splits. This measure must match the `algo` parameter. ### Caching and checkpointing -MLlib 1.2 adds several features for scaling up to larger (deeper) trees and tree ensembles. When `maxDepth` is set to be large, it can be useful to turn on node ID caching and checkpointing. These parameters are also useful for [RandomForest](api/scala/index.html#org.apache.spark.mllib.tree.RandomForest) when `numTrees` is set to be large. +MLlib 1.2 adds several features for scaling up to larger (deeper) trees and tree ensembles. When `maxDepth` is set to be large, it can be useful to turn on node ID caching and checkpointing. These parameters are also useful for [RandomForest](api/scala/index.html#org.apache.spark.mllib.tree.RandomForest$) when `numTrees` is set to be large. * **`useNodeIdCache`**: If this is set to true, the algorithm will avoid passing the current model (tree or trees) to executors on each iteration. * This can be useful with deep trees (speeding up computation on workers) and for large Random Forests (reducing communication on each iteration). |