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
 Basic statistics
 summary statistics
 correlations
 stratified sampling
 hypothesis testing
 random data generation
 Classification and regression
 Collaborative filtering
 alternating least squares (ALS)
 Clustering
 kmeans
 Dimensionality reduction
 singular value decomposition (SVD)
 principal component analysis (PCA)
 Feature extraction and transformation
 Optimization (developer)
 stochastic gradient descent
 limitedmemory BFGS (LBFGS)
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,
which depends on netlibjava,
and jblas.
netlibjava
and jblas
depend on native Fortran routines.
You need to install the
gfortran runtime library
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 netlibjava
’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 netlibjava
, please build Spark with Pnetliblgpl
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, 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 version 1.4 or newer.
Migration Guide
From 1.0 to 1.1
The only API changes in MLlib v1.1 are in
DecisionTree
,
which continues to be an experimental API in MLlib 1.1:

(Breaking change) The meaning of tree depth has been changed by 1 in order to match the implementations of trees in scikitlearn and in rpart. In MLlib v1.0, a depth1 tree had 1 leaf node, and a depth2 tree had 1 root node and 2 leaf nodes. In MLlib v1.1, a depth0 tree has 1 leaf node, and a depth1 tree has 1 root node and 2 leaf nodes. This depth is specified by the
maxDepth
parameter inStrategy
or viaDecisionTree
statictrainClassifier
andtrainRegressor
methods. 
(Nonbreaking change) We recommend using the newly added
trainClassifier
andtrainRegressor
methods to build aDecisionTree
, rather than using the old parameter classStrategy
. These new training methods explicitly separate classification and regression, and they replace specialized parameter types with simpleString
types.
Examples of the new, recommended trainClassifier
and trainRegressor
are given in the
Decision Trees 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.
We used to represent a feature vector by Array[Double]
, which is replaced by
Vector
in v1.0. Algorithms that used
to accept RDD[Array[Double]]
now take
RDD[Vector]
. LabeledPoint
is now a wrapper of (Double, Vector)
instead of (Double, Array[Double])
. Converting
Array[Double]
to Vector
is straightforward:
import org.apache.spark.mllib.linalg.{Vector, Vectors}
val array: Array[Double] = ... // a double array
val vector: Vector = Vectors.dense(array) // a dense vector
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
.
We used to represent a feature vector by double[]
, which is replaced by
Vector
in v1.0. Algorithms that used
to accept RDD<double[]>
now take
RDD<Vector>
. LabeledPoint
is now a wrapper of (double, Vector)
instead of (double, double[])
. Converting double[]
to
Vector
is straightforward:
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
Vectors
provides factory methods to
create sparse vectors.
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
, which takes both
dense and sparse feature vectors.
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]))