This page provides example notebooks showing how to use MLlib on Databricks.
Apache Spark MLlib is the Apache Spark machine learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, and underlying optimization primitives. For reference information about MLlib features, Databricks recommends the following Apache Spark API references:
For information about using Apache Spark MLlib from R, see the R machine learning documentation.
This notebook shows you how to build a binary classification application using the Apache Spark MLlib Pipelines API.
These examples demonstrate various applications of decision trees using the Apache Spark MLlib Pipelines API.
This notebook shows how to train an Apache Spark MLlib pipeline on historic data and apply it to streaming data.