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## What changes were proposed in this pull request? Fix the link at http://spark.apache.org/docs/latest/ml-guide.html. ## How was this patch tested? None Author: Sun Dapeng <sdp@apache.org> Closes #14386 from sundapeng/doclink.
215 lines
11 KiB
Markdown
215 lines
11 KiB
Markdown
---
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layout: global
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title: "MLlib: Main Guide"
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displayTitle: "Machine Learning Library (MLlib) Guide"
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---
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MLlib is Spark's machine learning (ML) library.
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Its goal is to make practical machine learning scalable and easy.
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At a high level, it provides tools such as:
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* ML Algorithms: common learning algorithms such as classification, regression, clustering, and collaborative filtering
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* Featurization: feature extraction, transformation, dimensionality reduction, and selection
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* Pipelines: tools for constructing, evaluating, and tuning ML Pipelines
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* Persistence: saving and load algorithms, models, and Pipelines
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* Utilities: linear algebra, statistics, data handling, etc.
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# Announcement: DataFrame-based API is primary API
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**The MLlib RDD-based API is now in maintenance mode.**
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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.
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The primary Machine Learning API for Spark is now the [DataFrame](sql-programming-guide.html)-based API in the `spark.ml` package.
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*What are the implications?*
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* MLlib will still support the RDD-based API in `spark.mllib` with bug fixes.
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* MLlib will not add new features to the RDD-based API.
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* In the Spark 2.x releases, MLlib will add features to the DataFrames-based API to reach feature parity with the RDD-based API.
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* After reaching feature parity (roughly estimated for Spark 2.2), the RDD-based API will be deprecated.
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* The RDD-based API is expected to be removed in Spark 3.0.
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*Why is MLlib switching to the DataFrame-based API?*
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* 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.
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* The DataFrame-based API for MLlib provides a uniform API across ML algorithms and across multiple languages.
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* DataFrames facilitate practical ML Pipelines, particularly feature transformations. See the [Pipelines guide](ml-pipeline.html) for details.
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# Dependencies
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MLlib uses the linear algebra package [Breeze](http://www.scalanlp.org/), which depends on
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[netlib-java](https://github.com/fommil/netlib-java) for optimised numerical processing.
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If native libraries[^1] are not available at runtime, you will see a warning message and a pure JVM
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implementation will be used instead.
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Due to licensing issues with runtime proprietary binaries, we do not include `netlib-java`'s native
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proxies by default.
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To configure `netlib-java` / Breeze to use system optimised binaries, include
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`com.github.fommil.netlib:all:1.1.2` (or build Spark with `-Pnetlib-lgpl`) as a dependency of your
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project and read the [netlib-java](https://github.com/fommil/netlib-java) documentation for your
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platform's additional installation instructions.
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To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4 or newer.
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[^1]: To learn more about the benefits and background of system optimised natives, you may wish to
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watch Sam Halliday's ScalaX talk on [High Performance Linear Algebra in Scala](http://fommil.github.io/scalax14/#/).
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# Migration guide
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MLlib is under active development.
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The APIs marked `Experimental`/`DeveloperApi` may change in future releases,
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and the migration guide below will explain all changes between releases.
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## From 1.6 to 2.0
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### Breaking changes
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There were several breaking changes in Spark 2.0, which are outlined below.
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**Linear algebra classes for DataFrame-based APIs**
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Spark's linear algebra dependencies were moved to a new project, `mllib-local`
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(see [SPARK-13944](https://issues.apache.org/jira/browse/SPARK-13944)).
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As part of this change, the linear algebra classes were copied to a new package, `spark.ml.linalg`.
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The DataFrame-based APIs in `spark.ml` now depend on the `spark.ml.linalg` classes,
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leading to a few breaking changes, predominantly in various model classes
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(see [SPARK-14810](https://issues.apache.org/jira/browse/SPARK-14810) for a full list).
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**Note:** the RDD-based APIs in `spark.mllib` continue to depend on the previous package `spark.mllib.linalg`.
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_Converting vectors and matrices_
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While most pipeline components support backward compatibility for loading,
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some existing `DataFrames` and pipelines in Spark versions prior to 2.0, that contain vector or matrix
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columns, may need to be migrated to the new `spark.ml` vector and matrix types.
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Utilities for converting `DataFrame` columns from `spark.mllib.linalg` to `spark.ml.linalg` types
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(and vice versa) can be found in `spark.mllib.util.MLUtils`.
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There are also utility methods available for converting single instances of
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vectors and matrices. Use the `asML` method on a `mllib.linalg.Vector` / `mllib.linalg.Matrix`
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for converting to `ml.linalg` types, and
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`mllib.linalg.Vectors.fromML` / `mllib.linalg.Matrices.fromML`
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for converting to `mllib.linalg` types.
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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{% highlight scala %}
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import org.apache.spark.mllib.util.MLUtils
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// convert DataFrame columns
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val convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF)
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val convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF)
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// convert a single vector or matrix
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val mlVec: org.apache.spark.ml.linalg.Vector = mllibVec.asML
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val mlMat: org.apache.spark.ml.linalg.Matrix = mllibMat.asML
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{% endhighlight %}
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Refer to the [`MLUtils` Scala docs](api/scala/index.html#org.apache.spark.mllib.util.MLUtils$) for further detail.
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</div>
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<div data-lang="java" markdown="1">
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{% highlight java %}
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import org.apache.spark.mllib.util.MLUtils;
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import org.apache.spark.sql.Dataset;
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// convert DataFrame columns
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Dataset<Row> convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF);
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Dataset<Row> convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF);
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// convert a single vector or matrix
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org.apache.spark.ml.linalg.Vector mlVec = mllibVec.asML();
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org.apache.spark.ml.linalg.Matrix mlMat = mllibMat.asML();
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{% endhighlight %}
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Refer to the [`MLUtils` Java docs](api/java/org/apache/spark/mllib/util/MLUtils.html) for further detail.
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</div>
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<div data-lang="python" markdown="1">
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{% highlight python %}
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from pyspark.mllib.util import MLUtils
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# convert DataFrame columns
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convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF)
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convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF)
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# convert a single vector or matrix
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mlVec = mllibVec.asML()
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mlMat = mllibMat.asML()
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{% endhighlight %}
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Refer to the [`MLUtils` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.util.MLUtils) for further detail.
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</div>
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</div>
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**Deprecated methods removed**
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Several deprecated methods were removed in the `spark.mllib` and `spark.ml` packages:
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* `setScoreCol` in `ml.evaluation.BinaryClassificationEvaluator`
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* `weights` in `LinearRegression` and `LogisticRegression` in `spark.ml`
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* `setMaxNumIterations` in `mllib.optimization.LBFGS` (marked as `DeveloperApi`)
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* `treeReduce` and `treeAggregate` in `mllib.rdd.RDDFunctions` (these functions are available on `RDD`s directly, and were marked as `DeveloperApi`)
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* `defaultStategy` in `mllib.tree.configuration.Strategy`
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* `build` in `mllib.tree.Node`
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* libsvm loaders for multiclass and load/save labeledData methods in `mllib.util.MLUtils`
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A full list of breaking changes can be found at [SPARK-14810](https://issues.apache.org/jira/browse/SPARK-14810).
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### Deprecations and changes of behavior
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**Deprecations**
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Deprecations in the `spark.mllib` and `spark.ml` packages include:
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* [SPARK-14984](https://issues.apache.org/jira/browse/SPARK-14984):
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In `spark.ml.regression.LinearRegressionSummary`, the `model` field has been deprecated.
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* [SPARK-13784](https://issues.apache.org/jira/browse/SPARK-13784):
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In `spark.ml.regression.RandomForestRegressionModel` and `spark.ml.classification.RandomForestClassificationModel`,
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the `numTrees` parameter has been deprecated in favor of `getNumTrees` method.
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* [SPARK-13761](https://issues.apache.org/jira/browse/SPARK-13761):
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In `spark.ml.param.Params`, the `validateParams` method has been deprecated.
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We move all functionality in overridden methods to the corresponding `transformSchema`.
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* [SPARK-14829](https://issues.apache.org/jira/browse/SPARK-14829):
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In `spark.mllib` package, `LinearRegressionWithSGD`, `LassoWithSGD`, `RidgeRegressionWithSGD` and `LogisticRegressionWithSGD` have been deprecated.
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We encourage users to use `spark.ml.regression.LinearRegresson` and `spark.ml.classification.LogisticRegresson`.
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* [SPARK-14900](https://issues.apache.org/jira/browse/SPARK-14900):
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In `spark.mllib.evaluation.MulticlassMetrics`, the parameters `precision`, `recall` and `fMeasure` have been deprecated in favor of `accuracy`.
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* [SPARK-15644](https://issues.apache.org/jira/browse/SPARK-15644):
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In `spark.ml.util.MLReader` and `spark.ml.util.MLWriter`, the `context` method has been deprecated in favor of `session`.
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* In `spark.ml.feature.ChiSqSelectorModel`, the `setLabelCol` method has been deprecated since it was not used by `ChiSqSelectorModel`.
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**Changes of behavior**
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Changes of behavior in the `spark.mllib` and `spark.ml` packages include:
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* [SPARK-7780](https://issues.apache.org/jira/browse/SPARK-7780):
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`spark.mllib.classification.LogisticRegressionWithLBFGS` directly calls `spark.ml.classification.LogisticRegresson` for binary classification now.
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This will introduce the following behavior changes for `spark.mllib.classification.LogisticRegressionWithLBFGS`:
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* The intercept will not be regularized when training binary classification model with L1/L2 Updater.
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* If users set without regularization, training with or without feature scaling will return the same solution by the same convergence rate.
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* [SPARK-13429](https://issues.apache.org/jira/browse/SPARK-13429):
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In order to provide better and consistent result with `spark.ml.classification.LogisticRegresson`,
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the default value of `spark.mllib.classification.LogisticRegressionWithLBFGS`: `convergenceTol` has been changed from 1E-4 to 1E-6.
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* [SPARK-12363](https://issues.apache.org/jira/browse/SPARK-12363):
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Fix a bug of `PowerIterationClustering` which will likely change its result.
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* [SPARK-13048](https://issues.apache.org/jira/browse/SPARK-13048):
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`LDA` using the `EM` optimizer will keep the last checkpoint by default, if checkpointing is being used.
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* [SPARK-12153](https://issues.apache.org/jira/browse/SPARK-12153):
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`Word2Vec` now respects sentence boundaries. Previously, it did not handle them correctly.
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* [SPARK-10574](https://issues.apache.org/jira/browse/SPARK-10574):
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`HashingTF` uses `MurmurHash3` as default hash algorithm in both `spark.ml` and `spark.mllib`.
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* [SPARK-14768](https://issues.apache.org/jira/browse/SPARK-14768):
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The `expectedType` argument for PySpark `Param` was removed.
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* [SPARK-14931](https://issues.apache.org/jira/browse/SPARK-14931):
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Some default `Param` values, which were mismatched between pipelines in Scala and Python, have been changed.
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* [SPARK-13600](https://issues.apache.org/jira/browse/SPARK-13600):
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`QuantileDiscretizer` now uses `spark.sql.DataFrameStatFunctions.approxQuantile` to find splits (previously used custom sampling logic).
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The output buckets will differ for same input data and params.
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## Previous Spark versions
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Earlier migration guides are archived [on this page](ml-migration-guides.html).
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---
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