Language:
English
Pages:
1 online resource (1 video file, approximately 39 min.)
Edition:
1st edition
Keywords:
Electronic videos ; local
Abstract:
Feature engineering is generally the section that gets left out of machine learning books, but it’s also the most important part of successful models, even in today’s world of deep learning. While academic courses on machine learning focus on gradients and the latest flavor of recurrent network, Ted Dunning (MapR) explores the techniques that practitioners in the real world are seeking out better features and figuring out how to extract value using a variety of time-honored (and occasionally exceptionally clever) heuristics. In a sense, feature engineering is the Rodney Dangerfield of machine learning, never getting any respect. It is, however, the task that will get you the most value for time spent in terms of model performance. This work is not just the work of the data scientist. Good features encode business realities as well and are the cross-product of good business sense and good data engineering. Prerequisite knowledge A basic understanding of how machine learning is used to teach models What you'll learn Learn some surprising techniques that can help you solve some really hard problems This session is from the 2019 O'Reilly Strata Conference in New York, NY.
Note:
Online resource; Title from title screen (viewed February 28, 2020)
,
Mode of access: World Wide Web.
