Module 6: Regularization & Cross-Validation

Topic: Ridge regression, Lasso, Elastic Net, k-fold cross-validation, ROC/AUC.

Learning Objectives

Understand the bias-variance tradeoff
Fit regularized models with glmnet
Tune λ via cross-validation
Evaluate classifiers with ROC curves and AUC

Slides

Regularization and Cross-validation Slides (PDF)

R Code

Data

Key Packages

library(glmnet)
library(ggplot2)

Assignment

HW6: Regularization and Cross-Validation — due Module 7 Wednesday. Submit on Canvas.

---
title: "Module 6: Regularization & Cross-Validation"
---

::: {.week-header}
**Topic:** Ridge regression, Lasso, Elastic Net, k-fold cross-validation, ROC/AUC.
:::

## Learning Objectives

- Understand the bias-variance tradeoff
- Fit regularized models with `glmnet`
- Tune λ via cross-validation
- Evaluate classifiers with ROC curves and AUC

## Slides

::: {.file-list}
- [Regularization and Cross-validation Slides (PDF)](../Week%205/Regularization%20and%20Cross-validation%20slides.pdf)
:::

## R Code

::: {.file-list}
- [Regularization.R](../Week%206/Regularization.R)
- [Cross_validation.r](../Week%206/Cross_validation.r)
- [AUC.R](../Week%206/AUC.R)
- [ggplot_bonus_updated.R](../Week%206/ggplot_bonus_updated.R)
:::

## Data

::: {.file-list}
- [marmot.csv](../Week%206/marmot.csv)
- [drought.csv](../Week%206/drought.csv)
- [puffin.csv](../Week%206/puffin.csv)
:::

## Key Packages

```r
library(glmnet)
library(ggplot2)
```

## Assignment

**HW6: Regularization and Cross-Validation** — due Module 7 Wednesday. Submit on Canvas.