ML 9.2Lesson outlineLesson 2 of 5 in Module 9
Random Forests
Average many deep trees grown on bootstrap samples, decorrelated by a random subset of features, to cut variance, and read the out-of-bag error.
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Lesson outline: Random Forests
In this lesson12 parts
- 01One tree is unstable
- 02The forest cells again
- 03What to beat
- 04Resample the rows, refit the tree
- 05Decorrelate the trees
- 06Why averaging helps
- 07How many columns?
- 08How many trees?
- 09Out of bag, against five folds
- 10Where it misleads
- 11Code
- 12Recap, challenge, and what comes next
Builds on
This lesson stands on its own. These go deeper into what it uses.