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08.Rmd
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# Tree-Based Methods
**Learning objectives:**
- Use **basic decision trees** to model relationships between predictors and an outcome.
- Compare and contrast tree-based models with other model types.
- Use **tree-based ensemble methods** to build predictive models.
- Compare and contrast the various methods of building tree ensembles: bagging, boosting, random forests, and Bayesian Additive Regression Trees.
## Slide 1
- Try to follow a slide-like format.
## Slide 2
- Use `##` to create new slides (sections).
## Meeting Videos
### Cohort 1
`r knitr::include_url("https://www.youtube.com/embed/3E4Pu2UspDI")`
<details>
<summary> Meeting chat log </summary>
```
00:12:39 Ryan Metcalf: (I’m very self-conscious)
00:21:31 Mei Ling Soh: I didn’t have time to read this chapter, so sorry to ask, how did you decide on the internal nodes?
00:22:57 Jon Harmon (jonthegeek): I think we're about to go into that :)
00:23:08 shamsuddeen: I guess it is calculated based on the purity
```
</details>
`r knitr::include_url("https://www.youtube.com/embed/c_u4Gh4CDnM")`
<details>
<summary> Meeting chat log </summary>
```
00:43:03 jonathan.bratt: Maybe “inversely correlated” is more accurate?
```
</details>
`r knitr::include_url("https://www.youtube.com/embed/8NGM8aLRCv0")`
<details>
<summary> Meeting chat log </summary>
```
00:09:47 Jon Harmon (jonthegeek): Yay for using anything other than Boston 😁
00:14:51 jonathan.bratt: BTW:
00:15:07 jonathan.bratt: node), split, n, loss, yval, (yprob)
* denotes terminal node
00:15:40 jonathan.bratt: From `rpart` output
00:21:47 Federica Gazzelloni: I need to jump off in 15 minutes..apologies..
00:34:36 Federica Gazzelloni: See you on Thursday @laura - thanks @jon
```
</details>
### Cohort 2
`r knitr::include_url("https://www.youtube.com/embed/URL")`
<details>
<summary> Meeting chat log </summary>
```
ADD LOG HERE
```
</details>
### Cohort 3
`r knitr::include_url("https://www.youtube.com/embed/URL")`
<details>
<summary> Meeting chat log </summary>
```
ADD LOG HERE
```
</details>