You're reading from scikit-learn Cookbook - Second Edition

Product typeBook

Published inNov 2017

Reading LevelIntermediate

PublisherPackt

ISBN-139781787286382

Edition2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1)

Trent Hauck

Feature selection on L1 norms

We're going to work with some ideas that are similar to those we saw in the recipe on LASSO regression. In that recipe, we looked at the number of features that had zero coefficients. Now we're going to take this a step further and use the sparseness associated with L1 norms to pre-process the features.

Getting ready

We'll use the diabetes dataset to fit a regression. First, we'll fit a basic linear regression model with a ShuffleSplit cross-validation. After we do that, we'll use LASSO regression to find the coefficients that are zero when using an L1 penalty. This hopefully will help us to avoid overfitting (when the model is too specific to the data it was trained on...

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scikit-learn Cookbook - Second Edition

Published in: Nov 2017Publisher: PacktISBN-13: 9781787286382

Author (1)

Trent Hauck

Trent Hauck is a data scientist living and working in the Seattle area. He grew up in Wichita, Kansas and received his undergraduate and graduate degrees from the University of Kansas. He is the author of the book Instant Data Intensive Apps with pandas How-to, Packt Publishing—a book that can get you up to speed quickly with pandas and other associated technologies.
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