Practical feature engineering / Ted Dunning.

Format:

Video

Author/Creator:

Dunning, Ted, on-screen presenter.

Language:

English

Subjects (All):

Machine learning.

Computer engineering--Data processing.

Computer engineering.

Business enterprises--Data processing.

Business enterprises.

Information technology--Management.

Information technology.

O'Reilly Strata Data Conference (2019 : New York, New York).

O'Reilly Strata Data Conference.

Physical Description:

1 online resource (1 streaming video file (38 min., 49 sec.)) : digital, sound, color

Place of Publication:

[Place of publication not identified] : O'Reilly Media, 2019.

Summary:

"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. This session is from the 2019 O'Reilly Strata Conference in New York, NY."--Resource description page.

Participant:

Presenter, Ted Dunning.

Notes:

Title from title screen (viewed July 23, 2020).

OCLC:

1177145190