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
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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