---
layout: global
title: "MLlib: Main Guide"
displayTitle: "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](programming-guide.html#resilient-distributed-datasets-rdds)-based APIs in the `spark.mllib` package have entered maintenance mode.
The primary Machine Learning API for Spark is now the [DataFrame](sql-programming-guide.html)-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.
* 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](ml-pipeline.html) for details.
# Dependencies
MLlib uses the linear algebra package [Breeze](http://www.scalanlp.org/), which depends on
[netlib-java](https://github.com/fommil/netlib-java) for optimised numerical processing.
If native libraries[^1] 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 `netlib-java`'s native
proxies by default.
To configure `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](https://github.com/fommil/netlib-java) documentation for your
platform's additional installation instructions.
To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4 or newer.
[^1]: To learn more about the benefits and background of system optimised natives, you may wish to
watch Sam Halliday's ScalaX talk on [High Performance Linear Algebra in Scala](http://fommil.github.io/scalax14/#/).
# Migration guide
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.
## From 1.6 to 2.0
### Breaking changes
There were several breaking changes in Spark 2.0, which are outlined below.
**Linear algebra classes for DataFrame-based APIs**
Spark's linear algebra dependencies were moved to a new project, `mllib-local`
(see [SPARK-13944](https://issues.apache.org/jira/browse/SPARK-13944)).
As part of this change, the linear algebra classes were copied to a new package, `spark.ml.linalg`.
The DataFrame-based APIs in `spark.ml` now depend on the `spark.ml.linalg` classes,
leading to a few breaking changes, predominantly in various model classes
(see [SPARK-14810](https://issues.apache.org/jira/browse/SPARK-14810) for a full list).
**Note:** the RDD-based APIs in `spark.mllib` continue to depend on the previous package `spark.mllib.linalg`.
_Converting vectors and matrices_
While most pipeline components support backward compatibility for loading,
some existing `DataFrames` and pipelines in Spark versions prior to 2.0, that contain vector or matrix
columns, may need to be migrated to the new `spark.ml` vector and matrix types.
Utilities for converting `DataFrame` columns from `spark.mllib.linalg` to `spark.ml.linalg` types
(and vice versa) can be found in `spark.mllib.util.MLUtils`.
There are also utility methods available for converting single instances of
vectors and matrices. Use the `asML` method on a `mllib.linalg.Vector` / `mllib.linalg.Matrix`
for converting to `ml.linalg` types, and
`mllib.linalg.Vectors.fromML` / `mllib.linalg.Matrices.fromML`
for converting to `mllib.linalg` types.
<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
import org.apache.spark.mllib.util.MLUtils
// convert DataFrame columns
val convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF)
val convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF)
// convert a single vector or matrix
val mlVec: org.apache.spark.ml.linalg.Vector = mllibVec.asML
val mlMat: org.apache.spark.ml.linalg.Matrix = mllibMat.asML
{% endhighlight %}
Refer to the [`MLUtils` Scala docs](api/scala/index.html#org.apache.spark.mllib.util.MLUtils$) for further detail.
</div>
<div data-lang="java" markdown="1">
{% highlight java %}
import org.apache.spark.mllib.util.MLUtils;
import org.apache.spark.sql.Dataset;
// convert DataFrame columns
Dataset<Row> convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF);
Dataset<Row> convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF);
// convert a single vector or matrix
org.apache.spark.ml.linalg.Vector mlVec = mllibVec.asML();
org.apache.spark.ml.linalg.Matrix mlMat = mllibMat.asML();
{% endhighlight %}
Refer to the [`MLUtils` Java docs](api/java/org/apache/spark/mllib/util/MLUtils.html) for further detail.
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
from pyspark.mllib.util import MLUtils
# convert DataFrame columns
convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF)
convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF)
# convert a single vector or matrix
mlVec = mllibVec.asML()
mlMat = mllibMat.asML()
{% endhighlight %}
Refer to the [`MLUtils` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.util.MLUtils) for further detail.
</div>
</div>
**Deprecated methods removed**
Several deprecated methods were removed in the `spark.mllib` and `spark.ml` packages:
* `setScoreCol` in `ml.evaluation.BinaryClassificationEvaluator`
* `weights` in `LinearRegression` and `LogisticRegression` in `spark.ml`
* `setMaxNumIterations` in `mllib.optimization.LBFGS` (marked as `DeveloperApi`)
* `treeReduce` and `treeAggregate` in `mllib.rdd.RDDFunctions` (these functions are available on `RDD`s directly, and were marked as `DeveloperApi`)
* `defaultStategy` in `mllib.tree.configuration.Strategy`
* `build` in `mllib.tree.Node`
* libsvm loaders for multiclass and load/save labeledData methods in `mllib.util.MLUtils`
A full list of breaking changes can be found at [SPARK-14810](https://issues.apache.org/jira/browse/SPARK-14810).
### Deprecations and changes of behavior
**Deprecations**
Deprecations in the `spark.mllib` and `spark.ml` packages include:
* [SPARK-14984](https://issues.apache.org/jira/browse/SPARK-14984):
In `spark.ml.regression.LinearRegressionSummary`, the `model` field has been deprecated.
* [SPARK-13784](https://issues.apache.org/jira/browse/SPARK-13784):
In `spark.ml.regression.RandomForestRegressionModel` and `spark.ml.classification.RandomForestClassificationModel`,
the `numTrees` parameter has been deprecated in favor of `getNumTrees` method.
* [SPARK-13761](https://issues.apache.org/jira/browse/SPARK-13761):
In `spark.ml.param.Params`, the `validateParams` method has been deprecated.
We move all functionality in overridden methods to the corresponding `transformSchema`.
* [SPARK-14829](https://issues.apache.org/jira/browse/SPARK-14829):
In `spark.mllib` package, `LinearRegressionWithSGD`, `LassoWithSGD`, `RidgeRegressionWithSGD` and `LogisticRegressionWithSGD` have been deprecated.
We encourage users to use `spark.ml.regression.LinearRegresson` and `spark.ml.classification.LogisticRegresson`.
* [SPARK-14900](https://issues.apache.org/jira/browse/SPARK-14900):
In `spark.mllib.evaluation.MulticlassMetrics`, the parameters `precision`, `recall` and `fMeasure` have been deprecated in favor of `accuracy`.
* [SPARK-15644](https://issues.apache.org/jira/browse/SPARK-15644):
In `spark.ml.util.MLReader` and `spark.ml.util.MLWriter`, the `context` method has been deprecated in favor of `session`.
* In `spark.ml.feature.ChiSqSelectorModel`, the `setLabelCol` method has been deprecated since it was not used by `ChiSqSelectorModel`.
**Changes of behavior**
Changes of behavior in the `spark.mllib` and `spark.ml` packages include:
* [SPARK-7780](https://issues.apache.org/jira/browse/SPARK-7780):
`spark.mllib.classification.LogisticRegressionWithLBFGS` directly calls `spark.ml.classification.LogisticRegresson` for binary classification now.
This will introduce the following behavior changes for `spark.mllib.classification.LogisticRegressionWithLBFGS`:
* The intercept will not be regularized when training binary classification model with L1/L2 Updater.
* If users set without regularization, training with or without feature scaling will return the same solution by the same convergence rate.
* [SPARK-13429](https://issues.apache.org/jira/browse/SPARK-13429):
In order to provide better and consistent result with `spark.ml.classification.LogisticRegresson`,
the default value of `spark.mllib.classification.LogisticRegressionWithLBFGS`: `convergenceTol` has been changed from 1E-4 to 1E-6.
* [SPARK-12363](https://issues.apache.org/jira/browse/SPARK-12363):
Fix a bug of `PowerIterationClustering` which will likely change its result.
* [SPARK-13048](https://issues.apache.org/jira/browse/SPARK-13048):
`LDA` using the `EM` optimizer will keep the last checkpoint by default, if checkpointing is being used.
* [SPARK-12153](https://issues.apache.org/jira/browse/SPARK-12153):
`Word2Vec` now respects sentence boundaries. Previously, it did not handle them correctly.
* [SPARK-10574](https://issues.apache.org/jira/browse/SPARK-10574):
`HashingTF` uses `MurmurHash3` as default hash algorithm in both `spark.ml` and `spark.mllib`.
* [SPARK-14768](https://issues.apache.org/jira/browse/SPARK-14768):
The `expectedType` argument for PySpark `Param` was removed.
* [SPARK-14931](https://issues.apache.org/jira/browse/SPARK-14931):
Some default `Param` values, which were mismatched between pipelines in Scala and Python, have been changed.
* [SPARK-13600](https://issues.apache.org/jira/browse/SPARK-13600):
`QuantileDiscretizer` now uses `spark.sql.DataFrameStatFunctions.approxQuantile` to find splits (previously used custom sampling logic).
The output buckets will differ for same input data and params.
## Previous Spark versions
Earlier migration guides are archived [on this page](ml-migration-guides.html).
---
