---
layout: global
title: "MLlib: Main Guide"
displayTitle: "Machine Learning Library (MLlib) Guide"
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  this work for additional information regarding copyright ownership.
  The ASF licenses this file to You under the Apache License, Version 2.0
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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:

* 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 MLlib RDD-based API is now in maintenance mode.**

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.
The primary Machine Learning API for Spark is now the [DataFrame](sql-programming-guide.html)-based API in the `spark.ml` package.

*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.

*Why is MLlib switching to the DataFrame-based API?*

* 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.html) for details.

*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.

# Dependencies

MLlib uses linear algebra packages [Breeze](http://www.scalanlp.org/) and [dev.ludovic.netlib](https://github.com/luhenry/netlib) for optimised numerical processing[^1]. Those packages may call native acceleration libraries such as [Intel MKL](https://software.intel.com/content/www/us/en/develop/tools/math-kernel-library.html) or [OpenBLAS](http://www.openblas.net) if they are available as system libraries or in runtime library paths.

However, native acceleration libraries can't be distributed with Spark. See [MLlib Linear Algebra Acceleration Guide](ml-linalg-guide.html) for how to enable accelerated linear algebra processing. If accelerated native libraries are not enabled, you will see a warning message like below and a pure JVM implementation will be used instead:
```
WARNING: Failed to load implementation from:dev.ludovic.netlib.blas.JNIBLAS
```

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/).

# Highlights in 3.0

The list below highlights some of the new features and enhancements added to MLlib in the `3.0`
release of Spark:

* Multiple columns support was added to `Binarizer` ([SPARK-23578](https://issues.apache.org/jira/browse/SPARK-23578)), `StringIndexer` ([SPARK-11215](https://issues.apache.org/jira/browse/SPARK-11215)), `StopWordsRemover` ([SPARK-29808](https://issues.apache.org/jira/browse/SPARK-29808)) and PySpark `QuantileDiscretizer` ([SPARK-22796](https://issues.apache.org/jira/browse/SPARK-22796)).
* Tree-Based Feature Transformation was added
([SPARK-13677](https://issues.apache.org/jira/browse/SPARK-13677)).
* Two new evaluators `MultilabelClassificationEvaluator` ([SPARK-16692](https://issues.apache.org/jira/browse/SPARK-16692)) and `RankingEvaluator` ([SPARK-28045](https://issues.apache.org/jira/browse/SPARK-28045)) were added.
* Sample weights support was added in `DecisionTreeClassifier/Regressor` ([SPARK-19591](https://issues.apache.org/jira/browse/SPARK-19591)), `RandomForestClassifier/Regressor` ([SPARK-9478](https://issues.apache.org/jira/browse/SPARK-9478)), `GBTClassifier/Regressor` ([SPARK-9612](https://issues.apache.org/jira/browse/SPARK-9612)),  `MulticlassClassificationEvaluator` ([SPARK-24101](https://issues.apache.org/jira/browse/SPARK-24101)), `RegressionEvaluator` ([SPARK-24102](https://issues.apache.org/jira/browse/SPARK-24102)), `BinaryClassificationEvaluator` ([SPARK-24103](https://issues.apache.org/jira/browse/SPARK-24103)), `BisectingKMeans` ([SPARK-30351](https://issues.apache.org/jira/browse/SPARK-30351)), `KMeans` ([SPARK-29967](https://issues.apache.org/jira/browse/SPARK-29967)) and `GaussianMixture` ([SPARK-30102](https://issues.apache.org/jira/browse/SPARK-30102)).
* R API for `PowerIterationClustering` was added
([SPARK-19827](https://issues.apache.org/jira/browse/SPARK-19827)).
* Added Spark ML listener for tracking ML pipeline status
([SPARK-23674](https://issues.apache.org/jira/browse/SPARK-23674)).
* Fit with validation set was added to Gradient Boosted Trees in Python
([SPARK-24333](https://issues.apache.org/jira/browse/SPARK-24333)).
* [`RobustScaler`](ml-features.html#robustscaler) transformer was added
([SPARK-28399](https://issues.apache.org/jira/browse/SPARK-28399)).
* [`Factorization Machines`](ml-classification-regression.html#factorization-machines) classifier and regressor were added
([SPARK-29224](https://issues.apache.org/jira/browse/SPARK-29224)).
* Gaussian Naive Bayes Classifier ([SPARK-16872](https://issues.apache.org/jira/browse/SPARK-16872)) and Complement Naive Bayes Classifier ([SPARK-29942](https://issues.apache.org/jira/browse/SPARK-29942)) were added.
* ML function parity between Scala and Python
([SPARK-28958](https://issues.apache.org/jira/browse/SPARK-28958)).
* `predictRaw` is made public in all the Classification models. `predictProbability` is made public in all the Classification models except `LinearSVCModel`
([SPARK-30358](https://issues.apache.org/jira/browse/SPARK-30358)).

# Migration Guide

The migration guide is now archived [on this page](ml-migration-guide.html).
