Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/8377#discussion_r37943540
--- Diff: docs/ml-guide.md ---
@@ -801,6 +801,173 @@ jsc.stop();
</div>
+## Example: Model Selection via Train Validation Split
+In addition to `CrossValidator` Spark also offers
+[`TrainValidationSplit`](api/scala/index.html#org.apache.spark.ml.tuning.TrainValidationSplit)
for hyper-parameter tuning.
+It randomly splits the input dataset into train and validation sets based
on ratio passed as parameter
+and use evaluation metric on the validation set to select the best model.
+The use is similar to `CrossValidator`, but simpler and less
computationally expensive.
+
+`TrainValidationSplit` takes an `Estimator`, a set of `ParamMap`s, and an
+[`Evaluator`](api/scala/index.html#org.apache.spark.ml.Evaluator).
+It begins by splitting the dataset into two parts using *trainRatio*
parameter
+which are used as separate training and test datasets. For example with
`$trainRatio=0.75$` (default),
+`TrainValidationSplit` will generate training and test dataset pair where
75% of the data is used for training and 25% for validation.
--- End diff --
nit: "will generate training..." -> "will generate a training..."
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
