docs/mllib-guide.md - spark - Git at Google

 ---
 layout: global
 title: MLlib
 displayTitle: Machine Learning Library (MLlib) Guide
 description: MLlib machine learning library overview for Spark SPARK_VERSION_SHORT
 ---

 MLlib is Spark's machine learning (ML) library.
 Its goal is to make practical machine learning scalable and easy.
 It consists of common learning algorithms and utilities, including classification, regression,
 clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization
 primitives and higher-level pipeline APIs.

 It divides into two packages:

 * [`spark.mllib`](mllib-guide.html#data-types-algorithms-and-utilities) contains the original API
   built on top of [RDDs](programming-guide.html#resilient-distributed-datasets-rdds).
 * [`spark.ml`](ml-guide.html) provides higher-level API
   built on top of [DataFrames](sql-programming-guide.html#dataframes) for constructing ML pipelines.

 Using `spark.ml` is recommended because with DataFrames the API is more versatile and flexible.
 But we will keep supporting `spark.mllib` along with the development of `spark.ml`.
 Users should be comfortable using `spark.mllib` features and expect more features coming.
 Developers should contribute new algorithms to `spark.ml` if they fit the ML pipeline concept well,
 e.g., feature extractors and transformers.

 We list major functionality from both below, with links to detailed guides.

 # spark.mllib: data types, algorithms, and utilities

 * [Data types](mllib-data-types.html)
 * [Basic statistics](mllib-statistics.html)
   * [summary statistics](mllib-statistics.html#summary-statistics)
   * [correlations](mllib-statistics.html#correlations)
   * [stratified sampling](mllib-statistics.html#stratified-sampling)
   * [hypothesis testing](mllib-statistics.html#hypothesis-testing)
   * [streaming significance testing](mllib-statistics.html#streaming-significance-testing)
   * [random data generation](mllib-statistics.html#random-data-generation)
 * [Classification and regression](mllib-classification-regression.html)
   * [linear models (SVMs, logistic regression, linear regression)](mllib-linear-methods.html)
   * [naive Bayes](mllib-naive-bayes.html)
   * [decision trees](mllib-decision-tree.html)
   * [ensembles of trees (Random Forests and Gradient-Boosted Trees)](mllib-ensembles.html)
   * [isotonic regression](mllib-isotonic-regression.html)
 * [Collaborative filtering](mllib-collaborative-filtering.html)
   * [alternating least squares (ALS)](mllib-collaborative-filtering.html#collaborative-filtering)
 * [Clustering](mllib-clustering.html)
   * [k-means](mllib-clustering.html#k-means)
   * [Gaussian mixture](mllib-clustering.html#gaussian-mixture)
   * [power iteration clustering (PIC)](mllib-clustering.html#power-iteration-clustering-pic)
   * [latent Dirichlet allocation (LDA)](mllib-clustering.html#latent-dirichlet-allocation-lda)
   * [bisecting k-means](mllib-clustering.html#bisecting-kmeans)
   * [streaming k-means](mllib-clustering.html#streaming-k-means)
 * [Dimensionality reduction](mllib-dimensionality-reduction.html)
   * [singular value decomposition (SVD)](mllib-dimensionality-reduction.html#singular-value-decomposition-svd)
   * [principal component analysis (PCA)](mllib-dimensionality-reduction.html#principal-component-analysis-pca)
 * [Feature extraction and transformation](mllib-feature-extraction.html)
 * [Frequent pattern mining](mllib-frequent-pattern-mining.html)
   * [FP-growth](mllib-frequent-pattern-mining.html#fp-growth)
   * [association rules](mllib-frequent-pattern-mining.html#association-rules)
   * [PrefixSpan](mllib-frequent-pattern-mining.html#prefix-span)
 * [Evaluation metrics](mllib-evaluation-metrics.html)
 * [PMML model export](mllib-pmml-model-export.html)
 * [Optimization (developer)](mllib-optimization.html)
   * [stochastic gradient descent](mllib-optimization.html#stochastic-gradient-descent-sgd)
   * [limited-memory BFGS (L-BFGS)](mllib-optimization.html#limited-memory-bfgs-l-bfgs)

 # spark.ml: high-level APIs for ML pipelines

 * [Overview: estimators, transformers and pipelines](ml-guide.html)
 * [Extracting, transforming and selecting features](ml-features.html)
 * [Classification and regression](ml-classification-regression.html)
 * [Clustering](ml-clustering.html)
 * [Advanced topics](ml-advanced.html)

 Some techniques are not available yet in spark.ml, most notably dimensionality reduction
 Users can seamlessly combine the implementation of these techniques found in `spark.mllib` with the rest of the algorithms found in `spark.ml`.

 # Dependencies

 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 natives libraries[^1] are not available at runtime, you will see a warning message and a pure JVM
 implementation will be used instead.

 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.

 To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4 or newer.

 [^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/#/).

 # Migration guide

 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 1.5 to 1.6

 There are no breaking API changes in the `spark.mllib` or `spark.ml` packages, but there are
 deprecations and changes of behavior.

 Deprecations:

 * [SPARK-11358](https://issues.apache.org/jira/browse/SPARK-11358):
  In `spark.mllib.clustering.KMeans`, the `runs` parameter has been deprecated.
 * [SPARK-10592](https://issues.apache.org/jira/browse/SPARK-10592):
  In `spark.ml.classification.LogisticRegressionModel` and
  `spark.ml.regression.LinearRegressionModel`, the `weights` field has been deprecated in favor of
  the new name `coefficients`.  This helps disambiguate from instance (row) "weights" given to
  algorithms.

 Changes of behavior:

 * [SPARK-7770](https://issues.apache.org/jira/browse/SPARK-7770):
  `spark.mllib.tree.GradientBoostedTrees`: `validationTol` has changed semantics in 1.6.
  Previously, it was a threshold for absolute change in error. Now, it resembles the behavior of
  `GradientDescent`'s `convergenceTol`: For large errors, it uses relative error (relative to the
  previous error); for small errors (`< 0.01`), it uses absolute error.
 * [SPARK-11069](https://issues.apache.org/jira/browse/SPARK-11069):
  `spark.ml.feature.RegexTokenizer`: Previously, it did not convert strings to lowercase before
  tokenizing. Now, it converts to lowercase by default, with an option not to. This matches the
  behavior of the simpler `Tokenizer` transformer.

 ## Previous Spark versions

 Earlier migration guides are archived [on this page](mllib-migration-guides.html).

 ---
	---
	layout: global
	title: MLlib
	displayTitle: Machine Learning Library (MLlib) Guide
	description: MLlib machine learning library overview for Spark SPARK_VERSION_SHORT
	---

	MLlib is Spark's machine learning (ML) library.
	Its goal is to make practical machine learning scalable and easy.
	It consists of common learning algorithms and utilities, including classification, regression,
	clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization
	primitives and higher-level pipeline APIs.

	It divides into two packages:

	* [`spark.mllib`](mllib-guide.html#data-types-algorithms-and-utilities) contains the original API
	built on top of [RDDs](programming-guide.html#resilient-distributed-datasets-rdds).
	* [`spark.ml`](ml-guide.html) provides higher-level API
	built on top of [DataFrames](sql-programming-guide.html#dataframes) for constructing ML pipelines.

	Using `spark.ml` is recommended because with DataFrames the API is more versatile and flexible.
	But we will keep supporting `spark.mllib` along with the development of `spark.ml`.
	Users should be comfortable using `spark.mllib` features and expect more features coming.
	Developers should contribute new algorithms to `spark.ml` if they fit the ML pipeline concept well,
	e.g., feature extractors and transformers.

	We list major functionality from both below, with links to detailed guides.

	# spark.mllib: data types, algorithms, and utilities

	* [Data types](mllib-data-types.html)
	* [Basic statistics](mllib-statistics.html)
	* [summary statistics](mllib-statistics.html#summary-statistics)
	* [correlations](mllib-statistics.html#correlations)
	* [stratified sampling](mllib-statistics.html#stratified-sampling)
	* [hypothesis testing](mllib-statistics.html#hypothesis-testing)
	* [streaming significance testing](mllib-statistics.html#streaming-significance-testing)
	* [random data generation](mllib-statistics.html#random-data-generation)
	* [Classification and regression](mllib-classification-regression.html)
	* [linear models (SVMs, logistic regression, linear regression)](mllib-linear-methods.html)
	* [naive Bayes](mllib-naive-bayes.html)
	* [decision trees](mllib-decision-tree.html)
	* [ensembles of trees (Random Forests and Gradient-Boosted Trees)](mllib-ensembles.html)
	* [isotonic regression](mllib-isotonic-regression.html)
	* [Collaborative filtering](mllib-collaborative-filtering.html)
	* [alternating least squares (ALS)](mllib-collaborative-filtering.html#collaborative-filtering)
	* [Clustering](mllib-clustering.html)
	* [k-means](mllib-clustering.html#k-means)
	* [Gaussian mixture](mllib-clustering.html#gaussian-mixture)
	* [power iteration clustering (PIC)](mllib-clustering.html#power-iteration-clustering-pic)
	* [latent Dirichlet allocation (LDA)](mllib-clustering.html#latent-dirichlet-allocation-lda)
	* [bisecting k-means](mllib-clustering.html#bisecting-kmeans)
	* [streaming k-means](mllib-clustering.html#streaming-k-means)
	* [Dimensionality reduction](mllib-dimensionality-reduction.html)
	* [singular value decomposition (SVD)](mllib-dimensionality-reduction.html#singular-value-decomposition-svd)
	* [principal component analysis (PCA)](mllib-dimensionality-reduction.html#principal-component-analysis-pca)
	* [Feature extraction and transformation](mllib-feature-extraction.html)
	* [Frequent pattern mining](mllib-frequent-pattern-mining.html)
	* [FP-growth](mllib-frequent-pattern-mining.html#fp-growth)
	* [association rules](mllib-frequent-pattern-mining.html#association-rules)
	* [PrefixSpan](mllib-frequent-pattern-mining.html#prefix-span)
	* [Evaluation metrics](mllib-evaluation-metrics.html)
	* [PMML model export](mllib-pmml-model-export.html)
	* [Optimization (developer)](mllib-optimization.html)
	* [stochastic gradient descent](mllib-optimization.html#stochastic-gradient-descent-sgd)
	* [limited-memory BFGS (L-BFGS)](mllib-optimization.html#limited-memory-bfgs-l-bfgs)

	# spark.ml: high-level APIs for ML pipelines

	* [Overview: estimators, transformers and pipelines](ml-guide.html)
	* [Extracting, transforming and selecting features](ml-features.html)
	* [Classification and regression](ml-classification-regression.html)
	* [Clustering](ml-clustering.html)
	* [Advanced topics](ml-advanced.html)

	Some techniques are not available yet in spark.ml, most notably dimensionality reduction
	Users can seamlessly combine the implementation of these techniques found in `spark.mllib` with the rest of the algorithms found in `spark.ml`.

	# Dependencies

	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 natives libraries[^1] are not available at runtime, you will see a warning message and a pure JVM
	implementation will be used instead.

	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.

	To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4 or newer.

	[^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/#/).

	# Migration guide

	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 1.5 to 1.6

	There are no breaking API changes in the `spark.mllib` or `spark.ml` packages, but there are
	deprecations and changes of behavior.

	Deprecations:

	* [SPARK-11358](https://issues.apache.org/jira/browse/SPARK-11358):
	In `spark.mllib.clustering.KMeans`, the `runs` parameter has been deprecated.
	* [SPARK-10592](https://issues.apache.org/jira/browse/SPARK-10592):
	In `spark.ml.classification.LogisticRegressionModel` and
	`spark.ml.regression.LinearRegressionModel`, the `weights` field has been deprecated in favor of
	the new name `coefficients`. This helps disambiguate from instance (row) "weights" given to
	algorithms.

	Changes of behavior:

	* [SPARK-7770](https://issues.apache.org/jira/browse/SPARK-7770):
	`spark.mllib.tree.GradientBoostedTrees`: `validationTol` has changed semantics in 1.6.
	Previously, it was a threshold for absolute change in error. Now, it resembles the behavior of
	`GradientDescent`'s `convergenceTol`: For large errors, it uses relative error (relative to the
	previous error); for small errors (`< 0.01`), it uses absolute error.
	* [SPARK-11069](https://issues.apache.org/jira/browse/SPARK-11069):
	`spark.ml.feature.RegexTokenizer`: Previously, it did not convert strings to lowercase before
	tokenizing. Now, it converts to lowercase by default, with an option not to. This matches the
	behavior of the simpler `Tokenizer` transformer.

	## Previous Spark versions

	Earlier migration guides are archived [on this page](mllib-migration-guides.html).

	---