# Multinomial Logistic Regression with Apache Spark

- Speaker: DB Tsai
- Date: June 20, 2014
- Location: Hacker Dojo, Mountain View, CA
- Host: Silicon Valley Machine Learning Meetup
- URL: http://www.meetup.com/Silicon-Valley-Machine-Learning/events/187398222/
- Slide: http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297
- Video: https://www.youtube.com/watch?v=rYwZ09b1B1c

Logistic Regression can not only be used for modeling binary outcomes but also multinomial outcome with some extension. In this talk, DB will talk about basic idea of binary logistic regression step by step, and then extend to multinomial one. He will show how easy it’s with Spark to parallelize this iterative algorithm by utilizing the in-memory RDD cache to scale horizontally (the numbers of training data.) However, there is mathematical limitation on scaling vertically (the numbers of training features) while many recent applications from document classification and computational linguistics are of this type. He will talk about how to address this problem by L-BFGS optimizer instead of Newton optimizer.

BIG DATA · COMPUTER · HADOOP · MACHINE LEARNING · PROGRAMING

Algorithm Hadoop L-BFGS Machine Learning MLlib Multinomial Logistic Regression Optimization Spark