[SPARK-14817][ML][MLLIB][DOC] Made DataFrame-based API primary in MLlib guide

## What changes were proposed in this pull request?

Made DataFrame-based API primary
* Spark doc menu bar and other places now link to ml-guide.html, not mllib-guide.html
* mllib-guide.html keeps RDD-specific list of features, with a link at the top redirecting people to ml-guide.html
* ml-guide.html includes a "maintenance mode" announcement about the RDD-based API
  * **Reviewers: please check this carefully**
* (minor) Titles for DF API no longer include "- spark.ml" suffix.  Titles for RDD API have "- RDD-based API" suffix
* Moved migration guide to ml-guide from mllib-guide
  * Also moved past guides from mllib-migration-guides to ml-migration-guides, with a redirect link on mllib-migration-guides
  * **Reviewers**: I did not change any of the content of the migration guides.

Reorganized DataFrame-based guide:
* ml-guide.html mimics the old mllib-guide.html page in terms of content: overview, migration guide, etc.
* Moved Pipeline description into ml-pipeline.html and moved tuning into ml-tuning.html
  * **Reviewers**: I did not change the content of these guides, except some intro text.
* Sidebar remains the same, but with pipeline and tuning sections added

Other:
* ml-classification-regression.html: Moved text about linear methods to new section in page

## How was this patch tested?

Generated docs locally

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #14213 from jkbradley/ml-guide-2.0.

1 files changed, 176 insertions, 285 deletions

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index dae86d8480..5abec63b7a 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -1,323 +1,214 @@
 ---
 layout: global
-title: "Overview: estimators, transformers and pipelines - spark.ml"
-displayTitle: "Overview: estimators, transformers and pipelines - spark.ml"
+title: "MLlib: Main Guide"
+displayTitle: "Machine Learning Library (MLlib) Guide"
 ---
 
+MLlib is Spark's machine learning (ML) library.
+Its goal is to make practical machine learning scalable and easy.
+At a high level, it provides tools such as:
 
-`\[
-\newcommand{\R}{\mathbb{R}}
-\newcommand{\E}{\mathbb{E}}
-\newcommand{\x}{\mathbf{x}}
-\newcommand{\y}{\mathbf{y}}
-\newcommand{\wv}{\mathbf{w}}
-\newcommand{\av}{\mathbf{\alpha}}
-\newcommand{\bv}{\mathbf{b}}
-\newcommand{\N}{\mathbb{N}}
-\newcommand{\id}{\mathbf{I}}
-\newcommand{\ind}{\mathbf{1}}
-\newcommand{\0}{\mathbf{0}}
-\newcommand{\unit}{\mathbf{e}}
-\newcommand{\one}{\mathbf{1}}
-\newcommand{\zero}{\mathbf{0}}
-\]`
+* ML Algorithms: common learning algorithms such as classification, regression, clustering, and collaborative filtering
+* Featurization: feature extraction, transformation, dimensionality reduction, and selection
+* Pipelines: tools for constructing, evaluating, and tuning ML Pipelines
+* Persistence: saving and load algorithms, models, and Pipelines
+* Utilities: linear algebra, statistics, data handling, etc.
 
+# Announcement: DataFrame-based API is primary API
 
-The `spark.ml` package aims to provide a uniform set of high-level APIs built on top of
-[DataFrames](sql-programming-guide.html#dataframes) that help users create and tune practical
-machine learning pipelines.
-See the [algorithm guides](#algorithm-guides) section below for guides on sub-packages of
-`spark.ml`, including feature transformers unique to the Pipelines API, ensembles, and more.
+**The MLlib RDD-based API is now in maintenance mode.**
 
-**Table of contents**
+As of Spark 2.0, the [RDD](programming-guide.html#resilient-distributed-datasets-rdds)-based APIs in the `spark.mllib` package have entered maintenance mode.
+The primary Machine Learning API for Spark is now the [DataFrame](sql-programming-guide.html)-based API in the `spark.ml` package.
 
-* This will become a table of contents (this text will be scraped).
-{:toc}
+*What are the implications?*
 
+* MLlib will still support the RDD-based API in `spark.mllib` with bug fixes.
+* MLlib will not add new features to the RDD-based API.
+* In the Spark 2.x releases, MLlib will add features to the DataFrames-based API to reach feature parity with the RDD-based API.
+* After reaching feature parity (roughly estimated for Spark 2.2), the RDD-based API will be deprecated.
+* The RDD-based API is expected to be removed in Spark 3.0.
 
-# Main concepts in Pipelines
+*Why is MLlib switching to the DataFrame-based API?*
 
-Spark ML standardizes APIs for machine learning algorithms to make it easier to combine multiple
-algorithms into a single pipeline, or workflow.
-This section covers the key concepts introduced by the Spark ML API, where the pipeline concept is
-mostly inspired by the [scikit-learn](http://scikit-learn.org/) project.
+* DataFrames provide a more user-friendly API than RDDs.  The many benefits of DataFrames include Spark Datasources, SQL/DataFrame queries, Tungsten and Catalyst optimizations, and uniform APIs across languages.
+* The DataFrame-based API for MLlib provides a uniform API across ML algorithms and across multiple languages.
+* DataFrames facilitate practical ML Pipelines, particularly feature transformations.  See the [Pipelines guide](ml-pipeline.md) for details.
 
-* **[`DataFrame`](ml-guide.html#dataframe)**: Spark ML uses `DataFrame` from Spark SQL as an ML
-  dataset, which can hold a variety of data types.
-  E.g., a `DataFrame` could have different columns storing text, feature vectors, true labels, and predictions.
+# Dependencies
 
-* **[`Transformer`](ml-guide.html#transformers)**: A `Transformer` is an algorithm which can transform one `DataFrame` into another `DataFrame`.
-E.g., an ML model is a `Transformer` which transforms a `DataFrame` with features into a `DataFrame` with predictions.
+MLlib uses the linear algebra package [Breeze](http://www.scalanlp.org/), which depends on
+[netlib-java](https://github.com/fommil/netlib-java) for optimised numerical processing.
+If native libraries[^1] are not available at runtime, you will see a warning message and a pure JVM
+implementation will be used instead.
 
-* **[`Estimator`](ml-guide.html#estimators)**: An `Estimator` is an algorithm which can be fit on a `DataFrame` to produce a `Transformer`.
-E.g., a learning algorithm is an `Estimator` which trains on a `DataFrame` and produces a model.
+Due to licensing issues with runtime proprietary binaries, we do not include `netlib-java`'s native
+proxies by default.
+To configure `netlib-java` / Breeze to use system optimised binaries, include
+`com.github.fommil.netlib:all:1.1.2` (or build Spark with `-Pnetlib-lgpl`) as a dependency of your
+project and read the [netlib-java](https://github.com/fommil/netlib-java) documentation for your
+platform's additional installation instructions.
 
-* **[`Pipeline`](ml-guide.html#pipeline)**: A `Pipeline` chains multiple `Transformer`s and `Estimator`s together to specify an ML workflow.
+To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4 or newer.
 
-* **[`Parameter`](ml-guide.html#parameters)**: All `Transformer`s and `Estimator`s now share a common API for specifying parameters.
+[^1]: To learn more about the benefits and background of system optimised natives, you may wish to
+    watch Sam Halliday's ScalaX talk on [High Performance Linear Algebra in Scala](http://fommil.github.io/scalax14/#/).
 
-## DataFrame
+# Migration guide
 
-Machine learning can be applied to a wide variety of data types, such as vectors, text, images, and structured data.
-Spark ML adopts the `DataFrame` from Spark SQL in order to support a variety of data types.
+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.
 
-`DataFrame` supports many basic and structured types; see the [Spark SQL datatype reference](sql-programming-guide.html#spark-sql-datatype-reference) for a list of supported types.
-In addition to the types listed in the Spark SQL guide, `DataFrame` can use ML [`Vector`](mllib-data-types.html#local-vector) types.
+## From 1.6 to 2.0
 
-A `DataFrame` can be created either implicitly or explicitly from a regular `RDD`.  See the code examples below and the [Spark SQL programming guide](sql-programming-guide.html) for examples.
+### Breaking changes
 
-Columns in a `DataFrame` are named.  The code examples below use names such as "text," "features," and "label."
+There were several breaking changes in Spark 2.0, which are outlined below.
 
-## Pipeline components
+**Linear algebra classes for DataFrame-based APIs**
 
-### Transformers
+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).
 
-A `Transformer` is an abstraction that includes feature transformers and learned models.
-Technically, a `Transformer` implements a method `transform()`, which converts one `DataFrame` into
-another, generally by appending one or more columns.
-For example:
+**Note:** the RDD-based APIs in `spark.mllib` continue to depend on the previous package `spark.mllib.linalg`.
 
-* A feature transformer might take a `DataFrame`, read a column (e.g., text), map it into a new
-  column (e.g., feature vectors), and output a new `DataFrame` with the mapped column appended.
-* A learning model might take a `DataFrame`, read the column containing feature vectors, predict the
-  label for each feature vector, and output a new `DataFrame` with predicted labels appended as a
-  column.
+_Converting vectors and matrices_
 
-### Estimators
+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`.
 
-An `Estimator` abstracts the concept of a learning algorithm or any algorithm that fits or trains on
-data.
-Technically, an `Estimator` implements a method `fit()`, which accepts a `DataFrame` and produces a
-`Model`, which is a `Transformer`.
-For example, a learning algorithm such as `LogisticRegression` is an `Estimator`, and calling
-`fit()` trains a `LogisticRegressionModel`, which is a `Model` and hence a `Transformer`.
-
-### Properties of pipeline components
-
-`Transformer.transform()`s and `Estimator.fit()`s are both stateless.  In the future, stateful algorithms may be supported via alternative concepts.
-
-Each instance of a `Transformer` or `Estimator` has a unique ID, which is useful in specifying parameters (discussed below).
-
-## Pipeline
-
-In machine learning, it is common to run a sequence of algorithms to process and learn from data.
-E.g., a simple text document processing workflow might include several stages:
-
-* Split each document's text into words.
-* Convert each document's words into a numerical feature vector.
-* Learn a prediction model using the feature vectors and labels.
-
-Spark ML represents such a workflow as a `Pipeline`, which consists of a sequence of
-`PipelineStage`s (`Transformer`s and `Estimator`s) to be run in a specific order.
-We will use this simple workflow as a running example in this section.
-
-### How it works
-
-A `Pipeline` is specified as a sequence of stages, and each stage is either a `Transformer` or an `Estimator`.
-These stages are run in order, and the input `DataFrame` is transformed as it passes through each stage.
-For `Transformer` stages, the `transform()` method is called on the `DataFrame`.
-For `Estimator` stages, the `fit()` method is called to produce a `Transformer` (which becomes part of the `PipelineModel`, or fitted `Pipeline`), and that `Transformer`'s `transform()` method is called on the `DataFrame`.
-
-We illustrate this for the simple text document workflow.  The figure below is for the *training time* usage of a `Pipeline`.
-
-<p style="text-align: center;">
-  <img
-    src="img/ml-Pipeline.png"
-    title="Spark ML Pipeline Example"
-    alt="Spark ML Pipeline Example"
-    width="80%"
-  />
-</p>
-
-Above, the top row represents a `Pipeline` with three stages.
-The first two (`Tokenizer` and `HashingTF`) are `Transformer`s (blue), and the third (`LogisticRegression`) is an `Estimator` (red).
-The bottom row represents data flowing through the pipeline, where cylinders indicate `DataFrame`s.
-The `Pipeline.fit()` method is called on the original `DataFrame`, which has raw text documents and labels.
-The `Tokenizer.transform()` method splits the raw text documents into words, adding a new column with words to the `DataFrame`.
-The `HashingTF.transform()` method converts the words column into feature vectors, adding a new column with those vectors to the `DataFrame`.
-Now, since `LogisticRegression` is an `Estimator`, the `Pipeline` first calls `LogisticRegression.fit()` to produce a `LogisticRegressionModel`.
-If the `Pipeline` had more stages, it would call the `LogisticRegressionModel`'s `transform()`
-method on the `DataFrame` before passing the `DataFrame` to the next stage.
-
-A `Pipeline` is an `Estimator`.
-Thus, after a `Pipeline`'s `fit()` method runs, it produces a `PipelineModel`, which is a
-`Transformer`.
-This `PipelineModel` is used at *test time*; the figure below illustrates this usage.
-
-<p style="text-align: center;">
-  <img
-    src="img/ml-PipelineModel.png"
-    title="Spark ML PipelineModel Example"
-    alt="Spark ML PipelineModel Example"
-    width="80%"
-  />
-</p>
-
-In the figure above, the `PipelineModel` has the same number of stages as the original `Pipeline`, but all `Estimator`s in the original `Pipeline` have become `Transformer`s.
-When the `PipelineModel`'s `transform()` method is called on a test dataset, the data are passed
-through the fitted pipeline in order.
-Each stage's `transform()` method updates the dataset and passes it to the next stage.
-
-`Pipeline`s and `PipelineModel`s help to ensure that training and test data go through identical feature processing steps.
-
-### Details
-
-*DAG `Pipeline`s*: A `Pipeline`'s stages are specified as an ordered array.  The examples given here are all for linear `Pipeline`s, i.e., `Pipeline`s in which each stage uses data produced by the previous stage.  It is possible to create non-linear `Pipeline`s as long as the data flow graph forms a Directed Acyclic Graph (DAG).  This graph is currently specified implicitly based on the input and output column names of each stage (generally specified as parameters).  If the `Pipeline` forms a DAG, then the stages must be specified in topological order.
-
-*Runtime checking*: Since `Pipeline`s can operate on `DataFrame`s with varied types, they cannot use
-compile-time type checking.
-`Pipeline`s and `PipelineModel`s instead do runtime checking before actually running the `Pipeline`.
-This type checking is done using the `DataFrame` *schema*, a description of the data types of columns in the `DataFrame`.
-
-*Unique Pipeline stages*: A `Pipeline`'s stages should be unique instances.  E.g., the same instance
-`myHashingTF` should not be inserted into the `Pipeline` twice since `Pipeline` stages must have
-unique IDs.  However, different instances `myHashingTF1` and `myHashingTF2` (both of type `HashingTF`)
-can be put into the same `Pipeline` since different instances will be created with different IDs.
-
-## Parameters
-
-Spark ML `Estimator`s and `Transformer`s use a uniform API for specifying parameters.
-
-A `Param` is a named parameter with self-contained documentation.
-A `ParamMap` is a set of (parameter, value) pairs.
-
-There are two main ways to pass parameters to an algorithm:
-
-1. Set parameters for an instance.  E.g., if `lr` is an instance of `LogisticRegression`, one could
-   call `lr.setMaxIter(10)` to make `lr.fit()` use at most 10 iterations.
-   This API resembles the API used in `spark.mllib` package.
-2. Pass a `ParamMap` to `fit()` or `transform()`.  Any parameters in the `ParamMap` will override parameters previously specified via setter methods.
-
-Parameters belong to specific instances of `Estimator`s and `Transformer`s.
-For example, if we have two `LogisticRegression` instances `lr1` and `lr2`, then we can build a `ParamMap` with both `maxIter` parameters specified: `ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)`.
-This is useful if there are two algorithms with the `maxIter` parameter in a `Pipeline`.
-
-## Saving and Loading Pipelines
-
-Often times it is worth it to save a model or a pipeline to disk for later use. In Spark 1.6, a model import/export functionality was added to the Pipeline API. Most basic transformers are supported as well as some of the more basic ML models. Please refer to the algorithm's API documentation to see if saving and loading is supported.
-
-# Code examples
-
-This section gives code examples illustrating the functionality discussed above.
-For more info, please refer to the API documentation
-([Scala](api/scala/index.html#org.apache.spark.ml.package),
-[Java](api/java/org/apache/spark/ml/package-summary.html),
-and [Python](api/python/pyspark.ml.html)).
-Some Spark ML algorithms are wrappers for `spark.mllib` algorithms, and the
-[MLlib programming guide](mllib-guide.html) has details on specific algorithms.
-
-## Example: Estimator, Transformer, and Param
-
-This example covers the concepts of `Estimator`, `Transformer`, and `Param`.
+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">
 
-<div data-lang="scala">
-{% include_example scala/org/apache/spark/examples/ml/EstimatorTransformerParamExample.scala %}
-</div>
+{% highlight scala %}
+import org.apache.spark.mllib.util.MLUtils
 
-<div data-lang="java">
-{% include_example java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java %}
-</div>
+// 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 %}
 
-<div data-lang="python">
-{% include_example python/ml/estimator_transformer_param_example.py %}
-</div>
-
-</div>
-
-## Example: Pipeline
-
-This example follows the simple text document `Pipeline` illustrated in the figures above.
-
-<div class="codetabs">
-
-<div data-lang="scala">
-{% include_example scala/org/apache/spark/examples/ml/PipelineExample.scala %}
-</div>
-
-<div data-lang="java">
-{% include_example java/org/apache/spark/examples/ml/JavaPipelineExample.java %}
-</div>
-
-<div data-lang="python">
-{% include_example python/ml/pipeline_example.py %}
-</div>
-
-</div>
-
-## Example: model selection via cross-validation
-
-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.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.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 overridden by the `setMetricName`
-method in each of these evaluators.
-
-The `ParamMap` which produces the best evaluation metric (averaged over the `$k$` folds) is selected as the best model.
-`CrossValidator` finally fits the `Estimator` using the best `ParamMap` and the entire dataset.
-
-The following example demonstrates using `CrossValidator` to select from a grid of parameters.
-To help construct the parameter grid, we use the [`ParamGridBuilder`](api/scala/index.html#org.apache.spark.ml.tuning.ParamGridBuilder) utility.
-
-Note that cross-validation over a grid of parameters is expensive.
-E.g., in the example below, the parameter grid has 3 values for `hashingTF.numFeatures` and 2 values for `lr.regParam`, and `CrossValidator` uses 2 folds.  This multiplies out to `$(3 \times 2) \times 2 = 12$` different models being trained.
-In realistic settings, it can be common to try many more parameters and use more folds (`$k=3$` and `$k=10$` are common).
-In other words, using `CrossValidator` can be very expensive.
-However, it is also a well-established method for choosing parameters which is more statistically sound than heuristic hand-tuning.
-
-<div class="codetabs">
-
-<div data-lang="scala">
-{% include_example scala/org/apache/spark/examples/ml/ModelSelectionViaCrossValidationExample.scala %}
-</div>
-
-<div data-lang="java">
-{% include_example java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java %}
-</div>
-
-<div data-lang="python">
-
-{% include_example python/ml/cross_validator.py %}
-</div>
-
-</div>
-
-## Example: model selection via train validation split
-In addition to  `CrossValidator` Spark also offers `TrainValidationSplit` for hyper-parameter tuning.
-`TrainValidationSplit` only evaluates each combination of parameters once, as opposed to k times in
- the case of `CrossValidator`. It is therefore less expensive,
- but will not produce as reliable results when the training dataset is not sufficiently large.
-
-`TrainValidationSplit` takes an `Estimator`, a set of `ParamMap`s provided in the `estimatorParamMaps` parameter,
-and an `Evaluator`.
-It begins by splitting the dataset into two parts using the `trainRatio` parameter
-which are used as separate training and test datasets. For example with `$trainRatio=0.75$` (default),
-`TrainValidationSplit` will generate a training and test dataset pair where 75% of the data is used for training and 25% for validation.
-Similar to `CrossValidator`, `TrainValidationSplit` also iterates through the set of `ParamMap`s.
-For each combination of parameters, it trains the given `Estimator` and evaluates it using the given `Evaluator`.
-The `ParamMap` which produces the best evaluation metric is selected as the best option.
-`TrainValidationSplit` finally fits the `Estimator` using the best `ParamMap` and the entire dataset.
-
-<div class="codetabs">
-
-<div data-lang="scala" markdown="1">
-{% include_example scala/org/apache/spark/examples/ml/ModelSelectionViaTrainValidationSplitExample.scala %}
+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">
-{% include_example java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java %}
-</div>
 
-<div data-lang="python">
-{% include_example python/ml/train_validation_split.py %}
-</div>
+{% 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).
+
+### Deprecations and changes of behavior
+
+**Deprecations**
+
+Deprecations in the `spark.mllib` and `spark.ml` packages include:
+
+* [SPARK-14984](https://issues.apache.org/jira/browse/SPARK-14984):
+ In `spark.ml.regression.LinearRegressionSummary`, the `model` field has been deprecated.
+* [SPARK-13784](https://issues.apache.org/jira/browse/SPARK-13784):
+ In `spark.ml.regression.RandomForestRegressionModel` and `spark.ml.classification.RandomForestClassificationModel`,
+ the `numTrees` parameter has been deprecated in favor of `getNumTrees` method.
+* [SPARK-13761](https://issues.apache.org/jira/browse/SPARK-13761):
+ In `spark.ml.param.Params`, the `validateParams` method has been deprecated.
+ We move all functionality in overridden methods to the corresponding `transformSchema`.
+* [SPARK-14829](https://issues.apache.org/jira/browse/SPARK-14829):
+ In `spark.mllib` package, `LinearRegressionWithSGD`, `LassoWithSGD`, `RidgeRegressionWithSGD` and `LogisticRegressionWithSGD` have been deprecated.
+ We encourage users to use `spark.ml.regression.LinearRegresson` and `spark.ml.classification.LogisticRegresson`.
+* [SPARK-14900](https://issues.apache.org/jira/browse/SPARK-14900):
+ In `spark.mllib.evaluation.MulticlassMetrics`, the parameters `precision`, `recall` and `fMeasure` have been deprecated in favor of `accuracy`.
+* [SPARK-15644](https://issues.apache.org/jira/browse/SPARK-15644):
+ In `spark.ml.util.MLReader` and `spark.ml.util.MLWriter`, the `context` method has been deprecated in favor of `session`.
+* In `spark.ml.feature.ChiSqSelectorModel`, the `setLabelCol` method has been deprecated since it was not used by `ChiSqSelectorModel`.
+
+**Changes of behavior**
+
+Changes of behavior in the `spark.mllib` and `spark.ml` packages include:
+
+* [SPARK-7780](https://issues.apache.org/jira/browse/SPARK-7780):
+ `spark.mllib.classification.LogisticRegressionWithLBFGS` directly calls `spark.ml.classification.LogisticRegresson` for binary classification now.
+ This will introduce the following behavior changes for `spark.mllib.classification.LogisticRegressionWithLBFGS`:
+    * The intercept will not be regularized when training binary classification model with L1/L2 Updater.
+    * If users set without regularization, training with or without feature scaling will return the same solution by the same convergence rate.
+* [SPARK-13429](https://issues.apache.org/jira/browse/SPARK-13429):
+ In order to provide better and consistent result with `spark.ml.classification.LogisticRegresson`,
+ the default value of `spark.mllib.classification.LogisticRegressionWithLBFGS`: `convergenceTol` has been changed from 1E-4 to 1E-6.
+* [SPARK-12363](https://issues.apache.org/jira/browse/SPARK-12363):
+ Fix a bug of `PowerIterationClustering` which will likely change its result.
+* [SPARK-13048](https://issues.apache.org/jira/browse/SPARK-13048):
+ `LDA` using the `EM` optimizer will keep the last checkpoint by default, if checkpointing is being used.
+* [SPARK-12153](https://issues.apache.org/jira/browse/SPARK-12153):
+ `Word2Vec` now respects sentence boundaries. Previously, it did not handle them correctly.
+* [SPARK-10574](https://issues.apache.org/jira/browse/SPARK-10574):
+ `HashingTF` uses `MurmurHash3` as default hash algorithm in both `spark.ml` and `spark.mllib`.
+* [SPARK-14768](https://issues.apache.org/jira/browse/SPARK-14768):
+ The `expectedType` argument for PySpark `Param` was removed.
+* [SPARK-14931](https://issues.apache.org/jira/browse/SPARK-14931):
+ Some default `Param` values, which were mismatched between pipelines in Scala and Python, have been changed.
+* [SPARK-13600](https://issues.apache.org/jira/browse/SPARK-13600):
+ `QuantileDiscretizer` now uses `spark.sql.DataFrameStatFunctions.approxQuantile` to find splits (previously used custom sampling logic).
+ The output buckets will differ for same input data and params.
+
+## Previous Spark versions
+
+Earlier migration guides are archived [on this page](ml-migration-guides.html).
 
-</div>
+---