Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.

Login

Algorithms

  1. Home
  2. Algorithms
  3. Report

Hyperparameter optimization for machine learning

Users: 1 - Average Rating: 5.00

Choosing the correct hyperparameters for machine learning or deep learning models is one of the best ways to extract the last juice out of your models. In this article, I will show you some of the best ways to do hyperparameter tuning that are available today.
It is possible and recommended to search the hyper-parameter space for the best cross validation score.
Scientific Area:
Python
Language/Environments:
Learning, Optimization
Target Group:
Advanced
Link:
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
Cite as:
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830.
Author of the review:
Filipe Alves
University of Minho


Reviews

You have to login to leave a comment. If you are not registered click here

Stefano Zampini


Hyperparameter optimization is a fundamental step in building every ML model and GridSearchCV is a function of scikit-learn library which allows you to easily implement it
Back to the Algorithms List