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Posted to commits@spark.apache.org by sr...@apache.org on 2015/09/19 13:01:34 UTC
spark git commit: Fixed links to the API
Repository: spark
Updated Branches:
refs/heads/master d507f9c0b -> d83b6aae8
Fixed links to the API
Submitting this change on the master branch as requested in https://github.com/apache/spark/pull/8819#issuecomment-141505941
Author: Alexis Seigneurin <al...@gmail.com>
Closes #8838 from aseigneurin/patch-2.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/d83b6aae
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/d83b6aae
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/d83b6aae
Branch: refs/heads/master
Commit: d83b6aae8b4357c56779cc98804eb350ab8af62d
Parents: d507f9c
Author: Alexis Seigneurin <al...@gmail.com>
Authored: Sat Sep 19 12:01:22 2015 +0100
Committer: Sean Owen <so...@cloudera.com>
Committed: Sat Sep 19 12:01:22 2015 +0100
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docs/ml-guide.md | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/d83b6aae/docs/ml-guide.md
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diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index c5d7f99..0427ac6 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -619,13 +619,13 @@ for row in selected.collect():
An important task in ML is *model selection*, or using data to find the best model or parameters for a given task. This is also called *tuning*.
`Pipeline`s facilitate model selection by making it easy to tune an entire `Pipeline` at once, rather than tuning each element in the `Pipeline` separately.
-Currently, `spark.ml` supports model selection using the [`CrossValidator`](api/scala/index.html#org.apache.spark.ml.tuning.CrossValidator) class, which takes an `Estimator`, a set of `ParamMap`s, and an [`Evaluator`](api/scala/index.html#org.apache.spark.ml.Evaluator).
+Currently, `spark.ml` supports model selection using the [`CrossValidator`](api/scala/index.html#org.apache.spark.ml.tuning.CrossValidator) class, which takes an `Estimator`, a set of `ParamMap`s, and an [`Evaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.Evaluator).
`CrossValidator` begins by splitting the dataset into a set of *folds* which are used as separate training and test datasets; e.g., with `$k=3$` folds, `CrossValidator` will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing.
`CrossValidator` iterates through the set of `ParamMap`s. For each `ParamMap`, it trains the given `Estimator` and evaluates it using the given `Evaluator`.
-The `Evaluator` can be a [`RegressionEvaluator`](api/scala/index.html#org.apache.spark.ml.RegressionEvaluator)
-for regression problems, a [`BinaryClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.BinaryClassificationEvaluator)
-for binary data, or a [`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.MultiClassClassificationEvaluator)
+The `Evaluator` can be a [`RegressionEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.RegressionEvaluator)
+for regression problems, a [`BinaryClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.BinaryClassificationEvaluator)
+for binary data, or a [`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.MultiClassClassificationEvaluator)
for multiclass problems. The default metric used to choose the best `ParamMap` can be overriden by the `setMetric`
method in each of these evaluators.
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