Skip to Content
Hands-On Automated Machine Learning
book

Hands-On Automated Machine Learning

by Sibanjan Das, Umit Mert Cakmak
April 2018
Beginner to intermediate content levelBeginner to intermediate
282 pages
6h 52m
English
Packt Publishing
Content preview from Hands-On Automated Machine Learning

Summary

In this chapter, you learned about model parameters, hyperparameters, and configuration space. Let's review them quickly:

  • Model parameters: You can consider these as parameters to be learned during training time
  • Model hyperparameters: These are the parameters that you should define before the training run starts
  • Configuration space parameters: These parameters refer to any other parameter used for the environment that hosts your experiment

You have been introduced to common hyperparameter optimization methods, such as grid search and randomized search. Grid search and randomized search do not use the information produced from previous training runs and this is a disadvantage that Bayesian-based optimization methods address.

Bayesian-based ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Automated Machine Learning

Automated Machine Learning

Adnan Masood
R: Unleash Machine Learning Techniques

R: Unleash Machine Learning Techniques

Raghav Bali, Dipanjan Sarkar, Brett Lantz, Cory Lesmeister

Publisher Resources

ISBN: 9781788629898Supplemental Content