O'Reilly logo

Machine Learning with Spark - Second Edition by Nick Pentreath, Manpreet Singh Ghotra, Rajdeep Dua

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Supported machine learning algorithms by Spark

The following algorithms are supported by Spark ML:

  • Collaborative filtering
    • Alternating Least Squares (ALS): Collaborative filtering is often used for recommender systems. These techniques aim to fill the missing entries of a user-item association matrix. The spark.mllib currently supports model-based collaborative filtering. In this implementation, users and products are described by a small set of latent factors that can be used to predict missing entries. The spark.mllib uses the ALS algorithm to learn these latent factors.
  • Clustering: This is an unsupervised learning problem where the aim is to group subsets of entities with one another based on the notion of similarity. Clustering ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required