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Activity Number: 172 - Machine Learning and Algorithms
Type: Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract #324944 View Presentation
Title: There Has to Be an Easier Way: a Simple Alternative for Parameter Tuning of Supervised Learning Methods
Author(s): Jill Lundell*
Companies:
Keywords: supervised learning ; support vector machines ; statistical learning ; R ; boosted trees ; optimization
Abstract:

Several R packages can tune supervised learning methods, but some packages are so comprehensive they are difficult to use. Others are easier to use, but will only tune one or two methods. This paper presents an alternative R package that uses an optimizer to remove much of the frustration with parameter tuning for gradient boosting machines, support vector machines, and adaboost.


Authors who are presenting talks have a * after their name.

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