Machine Learning Library (MLlib) Guide

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-based APIs in the spark.mllib package have entered maintenance mode. The primary Machine Learning API for Spark is now the DataFrame-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 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 and netlib-java for optimised numerical processing¹. Those packages may call native acceleration libraries such as Intel MKL or OpenBLAS if they are available as system libraries or in runtime library paths.

Due to differing OSS licenses, netlib-java’s native proxies can’t be distributed with Spark. See MLlib Linear Algebra Acceleration Guide 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:

WARN BLAS: Failed to load implementation from:com.github.fommil.netlib.NativeSystemBLAS
WARN BLAS: Failed to load implementation from:com.github.fommil.netlib.NativeRefBLAS

To use MLlib in Python, you will need NumPy version 1.4 or newer.

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

Migration Guide

The migration guide is now archived on this page.