Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/8498#discussion_r38236867
--- Diff: docs/ml-guide.md ---
@@ -868,34 +859,4 @@ jsc.stop();
</div>
-# Dependencies
-
-Spark ML currently depends on MLlib and has the same dependencies.
-Please see the [MLlib Dependencies guide](mllib-guide.html#dependencies)
for more info.
-
-Spark ML also depends upon Spark SQL, but the relevant parts of Spark SQL
do not bring additional dependencies.
-
-# Migration Guide
-
-## From 1.3 to 1.4
-
-Several major API changes occurred, including:
-* `Param` and other APIs for specifying parameters
-* `uid` unique IDs for Pipeline components
-* Reorganization of certain classes
-Since the `spark.ml` API was an Alpha Component in Spark 1.3, we do not
list all changes here.
-
-However, now that `spark.ml` is no longer an Alpha Component, we will
provide details on any API changes for future releases.
-
-## From 1.2 to 1.3
-
-The main API changes are from Spark SQL. We list the most important
changes here:
-
-* The old
[SchemaRDD](http://spark.apache.org/docs/1.2.1/api/scala/index.html#org.apache.spark.sql.SchemaRDD)
has been replaced with
[DataFrame](api/scala/index.html#org.apache.spark.sql.DataFrame) with a
somewhat modified API. All algorithms in Spark ML which used to use SchemaRDD
now use DataFrame.
-* In Spark 1.2, we used implicit conversions from `RDD`s of `LabeledPoint`
into `SchemaRDD`s by calling `import sqlContext._` where `sqlContext` was an
instance of `SQLContext`. These implicits have been moved, so we now call
`import sqlContext.implicits._`.
-* Java APIs for SQL have also changed accordingly. Please see the
examples above and the [Spark SQL Programming
Guide](sql-programming-guide.html) for details.
-
-Other changes were in `LogisticRegression`:
-
-* The `scoreCol` output column (with default value "score") was renamed to
be `probabilityCol` (with default value "probability"). The type was
originally `Double` (for the probability of class 1.0), but it is now `Vector`
(for the probability of each class, to support multiclass classification in the
future).
-* In Spark 1.2, `LogisticRegressionModel` did not include an intercept.
In Spark 1.3, it includes an intercept; however, it will always be 0.0 since it
uses the default settings for
[spark.mllib.LogisticRegressionWithLBFGS](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS).
The option to use an intercept will be added in the future.
+---
--- End diff --
There is one footnote in `mllib-guide.md` with this PR. I should remove the
one in `ml-guide.md`.
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