StatQuest: Random Forests Part 1 - Building, Using and Evaluating

Random Forests make a simple, yet effective, machine learning method. They are made out of decision trees, but don't have the same problems with accuracy. In this video, I walk you through the steps to build, use and evaluate a random forest. NOTE: Random Forests are made from Decision Trees, so if you don't know about those, here's the Quest: https://youtu.be/_L39rN6gz7Y ALSO NOTE: This StatQuest is based on Leo Breiman's (one of the creators of Random Forests) website: https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider... Patreon: https://www.patreon.com/statquest ...or... YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join ...buying one of my books, a study guide, a t-shirt or hoodie, or a song from the StatQuest store... https://statquest.org/statquest-store/ ...or just donating to StatQuest! https://www.paypal.me/statquest Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: https://twitter.com/joshuastarmer 0:00 Awesome song and introduction 0:31 Motivation for using Random Forests 1:17 Step 1, create a bootstrapped dataset 2:23 Step 2, create a decision tree a random subset of variables at each step 4:00 Step 3, repeat steps 1 and 2 a bunch of times 4:40 Classifying a new sample with a Random Forest 5:41 Definition of Bagging 6:03 Evaluating a Random Forest 8:34 Optimizing the Random Forest Corrections: 3:18 I should have said the same feature (or variable) can be selected multiple times in a tree. Every time we select a subset of features to choose from, we choose from the full list of features, even if we have already used some of those features. Thus, a single feature can appear multiple times in a tree. 9:28 I say "square" when I meant to say "square root". #statquest #randomforest #ML