754f820035
## What changes were proposed in this pull request? Add AL2 license to metadata of all .md files. This seemed to be the tidiest way as it will get ignored by .md renderers and other tools. Attempts to write them as markdown comments revealed that there is no such standard thing. ## How was this patch tested? Doc build Closes #24243 from srowen/SPARK-26918. Authored-by: Sean Owen <sean.owen@databricks.com> Signed-off-by: Sean Owen <sean.owen@databricks.com>
2.8 KiB
2.8 KiB
| layout | title | displayTitle | license |
|---|---|---|---|
| global | PMML model export - RDD-based API | PMML model export - RDD-based API | Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
- Table of contents {:toc}
spark.mllib supported models
spark.mllib supports model export to Predictive Model Markup Language (PMML).
The table below outlines the spark.mllib models that can be exported to PMML and their equivalent PMML model.
| spark.mllib model | PMML model |
|---|---|
| KMeansModel | ClusteringModel |
| LinearRegressionModel | RegressionModel (functionName="regression") |
| RidgeRegressionModel | RegressionModel (functionName="regression") |
| LassoModel | RegressionModel (functionName="regression") |
| SVMModel | RegressionModel (functionName="classification" normalizationMethod="none") |
| Binary LogisticRegressionModel | RegressionModel (functionName="classification" normalizationMethod="logit") |
Examples
To export a supported `model` (see table above) to PMML, simply call `model.toPMML`.
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.
Refer to the KMeans Scala docs and Vectors Scala docs for details on the API.
Here a complete example of building a KMeansModel and print it out in PMML format: {% include_example scala/org/apache/spark/examples/mllib/PMMLModelExportExample.scala %}
For unsupported models, either you will not find a .toPMML method or an IllegalArgumentException will be thrown.