---
layout: global
title: Machine Learning Library (MLlib)
---

MLlib is Spark's scalable machine learning library consisting of common learning algorithms and utilities,
including classification, regression, clustering, collaborative
filtering, dimensionality reduction, as well as underlying optimization primitives, as outlined below:

* [Data types](mllib-basics.html)
* [Basic statistics](mllib-stats.html)
  * random data generation  
  * stratified sampling
  * summary statistics
  * hypothesis testing
* [Classification and regression](mllib-classification-regression.html)
  * [linear models (SVMs, logistic regression, linear regression)](mllib-linear-methods.html)
  * [decision trees](mllib-decision-tree.html)
  * [naive Bayes](mllib-naive-bayes.html)
* [Collaborative filtering](mllib-collaborative-filtering.html)
  * alternating least squares (ALS)
* [Clustering](mllib-clustering.html)
  * k-means
* [Dimensionality reduction](mllib-dimensionality-reduction.html)
  * singular value decomposition (SVD)
  * principal component analysis (PCA)
* [Feature extraction and transformation](mllib-feature-extraction.html)
* [Optimization (developer)](mllib-optimization.html)
  * stochastic gradient descent
  * limited-memory BFGS (L-BFGS)

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.

# Dependencies

MLlib uses the linear algebra package [Breeze](http://www.scalanlp.org/),
which depends on [netlib-java](https://github.com/fommil/netlib-java),
and [jblas](https://github.com/mikiobraun/jblas). 
`netlib-java` and `jblas` depend on native Fortran routines.
You need to install the
[gfortran runtime library](https://github.com/mikiobraun/jblas/wiki/Missing-Libraries)
if it is not already present on your nodes.
MLlib will throw a linking error if it cannot detect these libraries automatically.
Due to license issues, we do not include `netlib-java`'s native libraries in MLlib's
dependency set under default settings.
If no native library is available at runtime, you will see a warning message.
To use native libraries from `netlib-java`, please build Spark with `-Pnetlib-lgpl` or
include `com.github.fommil.netlib:all:1.1.2` as a dependency of your project.
If you want to use optimized BLAS/LAPACK libraries such as
[OpenBLAS](http://www.openblas.net/), please link its shared libraries to
`/usr/lib/libblas.so.3` and `/usr/lib/liblapack.so.3`, respectively.
BLAS/LAPACK libraries on worker nodes should be built without multithreading.

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

---

# Migration Guide

## From 0.9 to 1.0

In MLlib v1.0, we support both dense and sparse input in a unified way, which introduces a few
breaking changes.  If your data is sparse, please store it in a sparse format instead of dense to
take advantage of sparsity in both storage and computation. Details are described below.

<div class="codetabs">
<div data-lang="scala" markdown="1">

We used to represent a feature vector by `Array[Double]`, which is replaced by
[`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used
to accept `RDD[Array[Double]]` now take
`RDD[Vector]`. [`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint)
is now a wrapper of `(Double, Vector)` instead of `(Double, Array[Double])`. Converting
`Array[Double]` to `Vector` is straightforward:

{% highlight scala %}
import org.apache.spark.mllib.linalg.{Vector, Vectors}

val array: Array[Double] = ... // a double array
val vector: Vector = Vectors.dense(array) // a dense vector
{% endhighlight %}

[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to create sparse vectors.

*Note*. Scala imports `scala.collection.immutable.Vector` by default, so you have to import `org.apache.spark.mllib.linalg.Vector` explicitly to use MLlib's `Vector`.

</div>

<div data-lang="java" markdown="1">

We used to represent a feature vector by `double[]`, which is replaced by
[`Vector`](api/java/index.html?org/apache/spark/mllib/linalg/Vector.html) in v1.0. Algorithms that used
to accept `RDD<double[]>` now take
`RDD<Vector>`. [`LabeledPoint`](api/java/index.html?org/apache/spark/mllib/regression/LabeledPoint.html)
is now a wrapper of `(double, Vector)` instead of `(double, double[])`. Converting `double[]` to
`Vector` is straightforward:

{% highlight java %}
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.mllib.linalg.Vectors;

double[] array = ... // a double array
Vector vector = Vectors.dense(array); // a dense vector
{% endhighlight %}

[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to
create sparse vectors.

</div>

<div data-lang="python" markdown="1">

We used to represent a labeled feature vector in a NumPy array, where the first entry corresponds to
the label and the rest are features.  This representation is replaced by class
[`LabeledPoint`](api/python/pyspark.mllib.regression.LabeledPoint-class.html), which takes both
dense and sparse feature vectors.

{% highlight python %}
from pyspark.mllib.linalg import SparseVector
from pyspark.mllib.regression import LabeledPoint

# Create a labeled point with a positive label and a dense feature vector.
pos = LabeledPoint(1.0, [1.0, 0.0, 3.0])

# Create a labeled point with a negative label and a sparse feature vector.
neg = LabeledPoint(0.0, SparseVector(3, [0, 2], [1.0, 3.0]))
{% endhighlight %}
</div>
</div>