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---
layout: global
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title: PMML model export - RDD-based API
displayTitle: PMML model export - RDD-based API
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---
* Table of contents
{:toc}
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## spark.mllib supported models
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`spark.mllib` supports model export to Predictive Model Markup Language ([PMML](http://en.wikipedia.org/wiki/Predictive_Model_Markup_Language)).
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The table below outlines the `spark.mllib` models that can be exported to PMML and their equivalent PMML model.
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< table class = "table" >
< thead >
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< tr > < th > spark.mllib model< / th > < th > PMML model< / th > < / tr >
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< / thead >
< tbody >
< tr >
< td > KMeansModel< / td > < td > ClusteringModel< / td >
< / tr >
< tr >
< td > LinearRegressionModel< / td > < td > RegressionModel (functionName="regression")< / td >
< / tr >
< tr >
< td > RidgeRegressionModel< / td > < td > RegressionModel (functionName="regression")< / td >
< / tr >
< tr >
< td > LassoModel< / td > < td > RegressionModel (functionName="regression")< / td >
< / tr >
< tr >
< td > SVMModel< / td > < td > RegressionModel (functionName="classification" normalizationMethod="none")< / td >
< / tr >
< tr >
< td > Binary LogisticRegressionModel< / td > < td > RegressionModel (functionName="classification" normalizationMethod="logit")< / td >
< / tr >
< / tbody >
< / table >
## Examples
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To export a supported `model` (see table above) to PMML, simply call `model.toPMML` .
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As well as exporting the PMML model to a String (`model.toPMML` as in the example above), you can export the PMML model to other formats.
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Refer to the [`KMeans` Scala docs ](api/scala/index.html#org.apache.spark.mllib.clustering.KMeans ) and [`Vectors` Scala docs ](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$ ) for details on the API.
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Here a complete example of building a KMeansModel and print it out in PMML format:
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{% include_example scala/org/apache/spark/examples/mllib/PMMLModelExportExample.scala %}
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For unsupported models, either you will not find a `.toPMML` method or an `IllegalArgumentException` will be thrown.
< / div >
< / div >