06.Rmd

# Linear Model Selection and Regularization

**Learning objectives:**

- Select a subset of features to include in a linear model.
  - Compare and contrast the forward stepwise, backward stepwise, hybrid, and best subset methods of subset selection.
- Use shrinkage methods to constrain the flexibility of linear models.
  - Compare and contrast the lasso and ridge regression methods of shrinkage.
- Reduce the dimensionality of the data for a linear model.
  - Compare and contrast the PCR and PLS methods of dimension reduction.
- Explain the challenges that may occur when fitting linear models to high-dimensional data.

## 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/URL")`

<details>
<summary> Meeting chat log </summary>

```
ADD LOG HERE
```
</details>