How to tune hyperparameters for machine learning in R with the mikropml package (CC126)
A critical part of training a machine learning model is to correctly tune the hyperparameters. In this episode of Code Club, Pat shows how you can use the mikropml R package to identify and tune the hyperparameters for a variety of machine leaning methods. The data he uses is from a microbiome study his lab has published looking for biomarkers associated with colorectal cancer.
In this episode, Pat will use functions from the #mikropml R package and data handling functions from #purrr and #dplyr in #RStudio. The accompanying blog post can be found at https://www.riffomonas.org/code_club/....
If you're interested in taking an upcoming 3 day R workshop, email me at [email protected]!
R: https://r-project.org
RStudio: https://rstudio.com
Raw data: https://github.com/riffomonas/raw_dat...
Workshops: https://www.mothur.org/wiki/workshops
You can also find complete tutorials for learning R with the tidyverse using...
Microbial ecology data: https://www.riffomonas.org/minimalR/
General data: https://www.riffomonas.org/generalR/
0:00 What are hyperparameters and why do we care?
2:30 Evaluating default hyperparameters
5:59 Resetting range of hyperparameters
8:00 Performing multiple splits and evaluating performance
15:03 Tuning two hyperparameters
18:42 Finding possible hyperparameters and their defaults
20:52 Recap
Watch video How to tune hyperparameters for machine learning in R with the mikropml package (CC126) online without registration, duration hours minute second in high quality. This video was added by user Riffomonas Project 12 July 2021, don't forget to share it with your friends and acquaintances, it has been viewed on our site 2,28 once and liked it 4 people.