3 Popular Ways for Hyperparameter Tuning with Random Forest

Video Description

In this video we will cover 3 different methods for Hyperparameter tuning with a Random Forest Classifier. These will include Grid Search, Randomized Search, and Bayesian Search. After introducing these concepts, we'll work through an example on a toy dataset in Python, and compare the results from these different methods. The break-down of this video is as follows: Introduction 00:00 What is Hyperparameter Tuning? 00:42 Random Forest Hyperparameters 04:43 Jupyter Notebook Setup 07:05 Grid Search Python Example 09:51 Randomized Search Python Example 12:50 Bayesian Search Python Example 14:54 Conclusions 19:13 The best way to keep up-to-date with my video/blog content is to sign up for my monthly Newsletter! Please visit: https://insidelearningmachines.com/newsletter/ to register. The notebook presented here can be found at: https://github.com/insidelearningmachines/Blog/blob/main/Notebook%20XXXV%203%20Methods%20for%20Hyperparameter%20Tuning%20with%20Random%20Forest.ipynb This video is based off of an article on my blog. You can find that blog article here: https://insidelearningmachines.com/hyperparameter_tuning_with_random_forest/ My article describing the Random Forest algorithm can be found here: https://insidelearningmachines.com/build-a-random-forest-in-python/ The homepage of my blog is: https://insidelearningmachines.com Other social media includes: Twitter: https://twitter.com/inside_machines Facebook: https://www.facebook.com/Inside-Learning-Machines-112215488183517 #machinelearning #datascience #classification #ensembles #randomforest #insidelearningmachines