Use Apache Spark MLlib on Databricks
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.
Binary classification example notebook
This notebook shows you how to build a binary classification application using the Apache Spark MLlib Pipelines API.
Binary classification notebook
Decision trees example notebooks
These examples demonstrate various applications of decision trees using the Apache Spark MLlib Pipelines API.
Decision trees
These notebooks show you how to perform classifications with decision trees.
Decision trees for digit recognition notebook
Decision trees for SFO survey notebook
GBT regression using MLlib pipelines
This notebook shows you how to use MLlib pipelines to perform a regression using gradient boosted trees to predict bike rental counts (per hour) from information such as day of the week, weather, season, and so on.
Bike sharing regression notebook
Advanced Apache Spark MLlib notebook example
This notebook illustrates how to create a custom transformer.