--- 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-data-types.html) * [Basic statistics](mllib-statistics.html) * summary statistics * correlations * stratified sampling * hypothesis testing * random data generation * [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.