Algorithms

MLlib contains many algorithms and utilities.

ML algorithms include:

• Classification: logistic regression, naive Bayes,...
• Regression: generalized linear regression, survival regression,...
• Decision trees, random forests, and gradient-boosted trees
• Recommendation: alternating least squares (ALS)
• Clustering: K-means, Gaussian mixtures (GMMs),...
• Topic modeling: latent Dirichlet allocation (LDA)
• Frequent itemsets, association rules, and sequential pattern mining

ML workflow utilities include:

• Feature transformations: standardization, normalization, hashing,...
• ML Pipeline construction
• Model evaluation and hyper-parameter tuning
• ML persistence: saving and loading models and Pipelines

Other utilities include:

• Distributed linear algebra: SVD, PCA,...
• Statistics: summary statistics, hypothesis testing,...

Refer to the MLlib guide for usage examples.

Community

MLlib is developed as part of the Apache Spark project. It thus gets tested and updated with each Spark release.

If you have questions about the library, ask on the Spark mailing lists.

MLlib is still a rapidly growing project and welcomes contributions. If you'd like to submit an algorithm to MLlib, read how to contribute to Spark and send us a patch!

Getting started

To get started with MLlib:

• Download Spark. MLlib is included as a module.
• Read the MLlib guide, which includes various usage examples.
• Learn how to deploy Spark on a cluster if you'd like to run in distributed mode. You can also run locally on a multicore machine without any setup.