Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/16076#discussion_r90347291
--- Diff: docs/ml-guide.md ---
@@ -60,152 +60,37 @@ MLlib is under active development.
The APIs marked `Experimental`/`DeveloperApi` may change in future
releases,
and the migration guide below will explain all changes between releases.
-## From 1.6 to 2.0
+## From 2.0 to 2.1
### Breaking changes
-There were several breaking changes in Spark 2.0, which are outlined below.
-
-**Linear algebra classes for DataFrame-based APIs**
-
-Spark's linear algebra dependencies were moved to a new project,
`mllib-local`
-(see [SPARK-13944](https://issues.apache.org/jira/browse/SPARK-13944)).
-As part of this change, the linear algebra classes were copied to a new
package, `spark.ml.linalg`.
-The DataFrame-based APIs in `spark.ml` now depend on the `spark.ml.linalg`
classes,
-leading to a few breaking changes, predominantly in various model classes
-(see [SPARK-14810](https://issues.apache.org/jira/browse/SPARK-14810) for
a full list).
-
-**Note:** the RDD-based APIs in `spark.mllib` continue to depend on the
previous package `spark.mllib.linalg`.
-
-_Converting vectors and matrices_
-
-While most pipeline components support backward compatibility for loading,
-some existing `DataFrames` and pipelines in Spark versions prior to 2.0,
that contain vector or matrix
-columns, may need to be migrated to the new `spark.ml` vector and matrix
types.
-Utilities for converting `DataFrame` columns from `spark.mllib.linalg` to
`spark.ml.linalg` types
-(and vice versa) can be found in `spark.mllib.util.MLUtils`.
-
-There are also utility methods available for converting single instances
of
-vectors and matrices. Use the `asML` method on a `mllib.linalg.Vector` /
`mllib.linalg.Matrix`
-for converting to `ml.linalg` types, and
-`mllib.linalg.Vectors.fromML` / `mllib.linalg.Matrices.fromML`
-for converting to `mllib.linalg` types.
-
-<div class="codetabs">
-<div data-lang="scala" markdown="1">
-
-{% highlight scala %}
-import org.apache.spark.mllib.util.MLUtils
-
-// convert DataFrame columns
-val convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF)
-val convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF)
-// convert a single vector or matrix
-val mlVec: org.apache.spark.ml.linalg.Vector = mllibVec.asML
-val mlMat: org.apache.spark.ml.linalg.Matrix = mllibMat.asML
-{% endhighlight %}
-
-Refer to the [`MLUtils` Scala
docs](api/scala/index.html#org.apache.spark.mllib.util.MLUtils$) for further
detail.
-</div>
-
-<div data-lang="java" markdown="1">
-
-{% highlight java %}
-import org.apache.spark.mllib.util.MLUtils;
-import org.apache.spark.sql.Dataset;
-
-// convert DataFrame columns
-Dataset<Row> convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF);
-Dataset<Row> convertedMatrixDF =
MLUtils.convertMatrixColumnsToML(matrixDF);
-// convert a single vector or matrix
-org.apache.spark.ml.linalg.Vector mlVec = mllibVec.asML();
-org.apache.spark.ml.linalg.Matrix mlMat = mllibMat.asML();
-{% endhighlight %}
-
-Refer to the [`MLUtils` Java
docs](api/java/org/apache/spark/mllib/util/MLUtils.html) for further detail.
-</div>
-
-<div data-lang="python" markdown="1">
-
-{% highlight python %}
-from pyspark.mllib.util import MLUtils
-
-# convert DataFrame columns
-convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF)
-convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF)
-# convert a single vector or matrix
-mlVec = mllibVec.asML()
-mlMat = mllibMat.asML()
-{% endhighlight %}
-
-Refer to the [`MLUtils` Python
docs](api/python/pyspark.mllib.html#pyspark.mllib.util.MLUtils) for further
detail.
-</div>
-</div>
-
**Deprecated methods removed**
-Several deprecated methods were removed in the `spark.mllib` and
`spark.ml` packages:
-
-* `setScoreCol` in `ml.evaluation.BinaryClassificationEvaluator`
-* `weights` in `LinearRegression` and `LogisticRegression` in `spark.ml`
-* `setMaxNumIterations` in `mllib.optimization.LBFGS` (marked as
`DeveloperApi`)
-* `treeReduce` and `treeAggregate` in `mllib.rdd.RDDFunctions` (these
functions are available on `RDD`s directly, and were marked as `DeveloperApi`)
-* `defaultStategy` in `mllib.tree.configuration.Strategy`
-* `build` in `mllib.tree.Node`
-* libsvm loaders for multiclass and load/save labeledData methods in
`mllib.util.MLUtils`
-
-A full list of breaking changes can be found at
[SPARK-14810](https://issues.apache.org/jira/browse/SPARK-14810).
+* `setLabelCol` in `feature.ChiSqSelectorModel`
+* `numTrees` in `classification.RandomForestClassificationModel` (This now
refers to the Param called `numTrees`)
+* `numTrees` in `regression.RandomForestRegressionModel` (This now refers
to the Param called `numTrees`)
+* `model` in `regression.LinearRegressionSummary`
+* `validateParams` in `PipelineStage`
--- End diff --
Also: validateParams in Evaluator
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