Large-scale Lasso and Elastic-Net Regularized Generalized Linear Models

A session at Spark Summit 2015

Monday 15th June, 2015

1:00pm to 1:30pm

Nonlinear methods are widely used to produce higher performance compared with linear methods; however, nonlinear methods are generally more expensive in model size, training time, and scoring phase. With proper feature engineering techniques like polynomial expansion, the linear methods can be as competitive as those nonlinear methods. In the process of mapping the data to higher dimensional space, the linear methods will be subject to overfitting and instability of coefficients which can be addressed by penalization methods including Lasso and Elastic-Net. Finally, we'll show how to train linear models with Elastic-Net regularization using MLlib.

About the speakers

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Steven Hillion
This person is speaking at this event.
DB Tsai

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Time 1:00pm1:30pm PST

Date Mon 15th June 2015

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