Web: https://www.reddit.com/r/datascience/comments/xg9mqb/hyperparameter_tuning_and_feature_selection/

Sept. 17, 2022, 2 a.m. | /u/strappingyl

Data Science reddit.com

I’m an actuary pretending to be a data scientist, so forgive me if this is the stupidest question on the planet:

When performing hyperparameter tuning, is it better practice to perform feature selection before or after tuning? The question is irrespective of model architecture, but let’s assume we’re talking about a boosted tree approach if that’s important.

More explicitly, let’s say I have a bunch of features and take the kitchen-sink approach to filter to the most important features. Limited …

datascience feature feature selection

Research Scientists

@ ODU Research Foundation | Norfolk, Virginia

Embedded Systems Engineer (Robotics)

@ Neo Cybernetica | Bedford, New Hampshire

2023 Luis J. Alvarez and Admiral Grace M. Hopper Postdoc Fellowship in Computing Sciences

@ Lawrence Berkeley National Lab | San Francisco, CA

Senior Manager Data Scientist

@ NAV | Remote, US

Senior AI Research Scientist

@ Earth Species Project | Remote anywhere

Research Fellow- Center for Security and Emerging Technology (Multiple Opportunities)

@ University of California Davis | Washington, DC

Staff Fellow - Data Scientist

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Staff Fellow - Senior Data Engineer

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Tech Business Data Analyst

@ Fivesky | Alpharetta, GA

Senior Applied Scientist

@ Amazon.com | London, England, GBR

AI Researcher (Junior/Mid-level)

@ Charles River Analytics Inc. | Cambridge, MA

Data Engineer - Machine Learning & AI

@ Calabrio | Minneapolis, Minnesota, United States