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---
layout: global
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title: "MLlib: Main Guide"
displayTitle: "Machine Learning Library (MLlib) Guide"
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---
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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:
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* 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.
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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 ](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.
* 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.
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* After reaching feature parity (roughly estimated for Spark 2.3), 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.
* 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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*What is "Spark ML"?*
* "Spark ML" is not an official name but occasionally used to refer to the MLlib DataFrame-based API.
This is majorly due to the `org.apache.spark.ml` Scala package name used by the DataFrame-based API,
and the "Spark ML Pipelines" term we used initially to emphasize the pipeline concept.
*Is MLlib deprecated?*
* No. MLlib includes both the RDD-based API and the DataFrame-based API.
The RDD-based API is now in maintenance mode.
But neither API is deprecated, nor MLlib as a whole.
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# Dependencies
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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.
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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.
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The most popular native BLAS such as [Intel MKL ](https://software.intel.com/en-us/mkl ), [OpenBLAS ](http://www.openblas.net ), can use multiple threads in a single operation, which can conflict with Spark's execution model.
Configuring these BLAS implementations to use a single thread for operations may actually improve performance (see [SPARK-21305 ](https://issues.apache.org/jira/browse/SPARK-21305 )). It is usually optimal to match this to the number of cores each Spark task is configured to use, which is 1 by default and typically left at 1.
Please refer to resources like the following to understand how to configure the number of threads these BLAS implementations use: [Intel MKL ](https://software.intel.com/en-us/articles/recommended-settings-for-calling-intel-mkl-routines-from-multi-threaded-applications ) and [OpenBLAS ](https://github.com/xianyi/OpenBLAS/wiki/faq#multi-threaded ).
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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
watch Sam Halliday's ScalaX talk on [High Performance Linear Algebra in Scala ](http://fommil.github.io/scalax14/#/ ).
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# Highlights in 2.3
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The list below highlights some of the new features and enhancements added to MLlib in the `2.3`
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release of Spark:
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* Built-in support for reading images into a `DataFrame` was added
([SPARK-21866](https://issues.apache.org/jira/browse/SPARK-21866)).
* [`OneHotEncoderEstimator` ](ml-features.html#onehotencoderestimator ) was added, and should be
used instead of the existing `OneHotEncoder` transformer. The new estimator supports
transforming multiple columns.
* Multiple column support was also added to `QuantileDiscretizer` and `Bucketizer`
([SPARK-22397](https://issues.apache.org/jira/browse/SPARK-22397) and
[SPARK-20542 ](https://issues.apache.org/jira/browse/SPARK-20542 ))
* A new [`FeatureHasher` ](ml-features.html#featurehasher ) transformer was added
([SPARK-13969](https://issues.apache.org/jira/browse/SPARK-13969)).
* Added support for evaluating multiple models in parallel when performing cross-validation using
[`TrainValidationSplit` or `CrossValidator` ](ml-tuning.html )
([SPARK-19357](https://issues.apache.org/jira/browse/SPARK-19357)).
* Improved support for custom pipeline components in Python (see
[SPARK-21633 ](https://issues.apache.org/jira/browse/SPARK-21633 ) and
[SPARK-21542 ](https://issues.apache.org/jira/browse/SPARK-21542 )).
* `DataFrame` functions for descriptive summary statistics over vector columns
([SPARK-19634](https://issues.apache.org/jira/browse/SPARK-19634)).
* Robust linear regression with Huber loss
([SPARK-3181](https://issues.apache.org/jira/browse/SPARK-3181)).
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# Migration guide
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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.
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## From 2.2 to 2.3
### Breaking changes
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* The class and trait hierarchy for logistic regression model summaries was changed to be cleaner
and better accommodate the addition of the multi-class summary. This is a breaking change for user
code that casts a `LogisticRegressionTrainingSummary` to a
` BinaryLogisticRegressionTrainingSummary` . Users should instead use the `model.binarySummary`
method. See [SPARK-17139 ](https://issues.apache.org/jira/browse/SPARK-17139 ) for more detail
(_note_ this is an `Experimental` API). This _does not_ affect the Python `summary` method, which
will still work correctly for both multinomial and binary cases.
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### Deprecations and changes of behavior
**Deprecations**
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* `OneHotEncoder` has been deprecated and will be removed in `3.0` . It has been replaced by the
new [`OneHotEncoderEstimator` ](ml-features.html#onehotencoderestimator )
(see [SPARK-13030 ](https://issues.apache.org/jira/browse/SPARK-13030 )). **Note** that
`OneHotEncoderEstimator` will be renamed to `OneHotEncoder` in `3.0` (but
`OneHotEncoderEstimator` will be kept as an alias).
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**Changes of behavior**
* [SPARK-21027 ](https://issues.apache.org/jira/browse/SPARK-21027 ):
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The default parallelism used in `OneVsRest` is now set to 1 (i.e. serial). In `2.2` and
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earlier versions, the level of parallelism was set to the default threadpool size in Scala.
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* [SPARK-22156 ](https://issues.apache.org/jira/browse/SPARK-22156 ):
The learning rate update for `Word2Vec` was incorrect when `numIterations` was set greater than
`1` . This will cause training results to be different between `2.3` and earlier versions.
* [SPARK-21681 ](https://issues.apache.org/jira/browse/SPARK-21681 ):
Fixed an edge case bug in multinomial logistic regression that resulted in incorrect coefficients
when some features had zero variance.
* [SPARK-16957 ](https://issues.apache.org/jira/browse/SPARK-16957 ):
Tree algorithms now use mid-points for split values. This may change results from model training.
* [SPARK-14657 ](https://issues.apache.org/jira/browse/SPARK-14657 ):
Fixed an issue where the features generated by `RFormula` without an intercept were inconsistent
with the output in R. This may change results from model training in this scenario.
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## Previous Spark versions
Earlier migration guides are archived [on this page ](ml-migration-guides.html ).
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