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new 4e31555 [SPARK-30933][ML][DOCS] ML, GraphX 3.0 QA: Update user guide
for new features & APIs
4e31555 is described below
commit 4e3155546be6f0e4c244295b91164b112fc6c522
Author: Huaxin Gao <[email protected]>
AuthorDate: Wed Mar 18 13:21:24 2020 -0500
[SPARK-30933][ML][DOCS] ML, GraphX 3.0 QA: Update user guide for new
features & APIs
### What changes were proposed in this pull request?
Change ml-tuning.html.
### Why are the changes needed?
Add description for ```MultilabelClassificationEvaluator``` and
```RankingEvaluator```.
### Does this PR introduce any user-facing change?
Yes
before:
after:
### How was this patch tested?
Closes #27880 from huaxingao/spark-30933.
Authored-by: Huaxin Gao <[email protected]>
Signed-off-by: Sean Owen <[email protected]>
(cherry picked from commit d22c9f6c0de76b7f3df7c9c98fdb0303c29e8e73)
Signed-off-by: Sean Owen <[email protected]>
---
docs/ml-tuning.md | 8 +++++---
1 file changed, 5 insertions(+), 3 deletions(-)
diff --git a/docs/ml-tuning.md b/docs/ml-tuning.md
index 49e2368..274f195 100644
--- a/docs/ml-tuning.md
+++ b/docs/ml-tuning.md
@@ -65,9 +65,11 @@ At a high level, these model selection tools work as follows:
The `Evaluator` can be a
[`RegressionEvaluator`](api/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.html)
for regression problems, a
[`BinaryClassificationEvaluator`](api/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.html)
-for binary data, or a
[`MulticlassClassificationEvaluator`](api/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.html)
-for multiclass problems. The default metric used to choose the best `ParamMap`
can be overridden by the `setMetricName`
-method in each of these evaluators.
+for binary data, a
[`MulticlassClassificationEvaluator`](api/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.html)
+for multiclass problems, a
[`MultilabelClassificationEvaluator`](api/scala/org/apache/spark/ml/evaluation/MultilabelClassificationEvaluator.html)
+ for multi-label classifications, or a
+[`RankingEvaluator`](api/scala/org/apache/spark/ml/evaluation/RankingEvaluator.html)
for ranking problems. The default metric used to
+choose the best `ParamMap` can be overridden by the `setMetricName` method in
each of these evaluators.
To help construct the parameter grid, users can use the
[`ParamGridBuilder`](api/scala/org/apache/spark/ml/tuning/ParamGridBuilder.html)
utility.
By default, sets of parameters from the parameter grid are evaluated in
serial. Parameter evaluation can be done in parallel by setting `parallelism`
with a value of 2 or more (a value of 1 will be serial) before running model
selection with `CrossValidator` or `TrainValidationSplit`.
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