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.
What are the implications?
- MLlib will still support the RDD-based API in
spark.mllibwith 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.
- After reaching feature parity (roughly estimated for Spark 2.2), the RDD-based API will be deprecated.
- The RDD-based API is expected to be removed in Spark 3.0.
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.mlScala 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.
MLlib uses the linear algebra package Breeze, which depends on netlib-java for optimised numerical processing. If native libraries1 are not available at runtime, you will see a warning message and a pure JVM implementation will be used instead.
Due to licensing issues with runtime proprietary binaries, we do not include
proxies by default.
netlib-java / Breeze to use system optimised binaries, include
com.github.fommil.netlib:all:1.1.2 (or build Spark with
-Pnetlib-lgpl) as a dependency of your
project and read the netlib-java documentation for your
platform’s additional installation instructions.
To use MLlib in Python, you will need NumPy version 1.4 or newer.
MLlib is under active development.
The APIs marked
DeveloperApi may change in future releases,
and the migration guide below will explain all changes between releases.
From 2.0 to 2.1
Deprecated methods removed
classification.RandomForestClassificationModel(This now refers to the Param called
regression.RandomForestRegressionModel(This now refers to the Param called
Deprecations and changes of behavior
Deprecate all Param setter methods except for input/output column Params for
Changes of behavior
Fix a bug of
ChiSqSelectorwhich will likely change its result. Now
ChiSquareSelectoruse pValue rather than raw statistic to select a fixed number of top features.
KMeansreturns potentially fewer than k cluster centers in cases where k distinct centroids aren’t available or aren’t selected.
KMeansreduces the default number of steps from 5 to 2 for the k-means|| initialization mode.
Previous Spark versions
Earlier migration guides are archived on this page.