---
title: "Classification"
author: "Your name here!"
date: "02/24/2025"

---

**Abstract:**

This is a technical blog post of **both** an HTML file *and* [.qmd file](https://raw.githubusercontent.com/cd-public/D505/refs/heads/master/hws/src/classify.qmd) hosted on GitHub pages.

# 0. Quarto Type-setting

- This document is rendered with Quarto, and configured to embed an images using the `embed-resources` option in the header.
- If you wish to use a similar header, here's is the format specification for this document:

```email
format: 
  html:
    embed-resources: true
```

# 1. Setup

**Step Up Code:**

```{.r}
sh <- suppressPackageStartupMessages
sh(library(tidyverse))
sh(library(caret))
sh(library(naivebayes))
wine <- readRDS(gzcon(url("https://github.com/cd-public/D505/raw/master/dat/pinot.rds")))
```

# 2. Logistic Concepts

Why do we call it Logistic Regression even though we are using the technique for classification?

> <span style="color:red;font-weight:bold">TODO</span>: *Explain.*

# 3. Modeling

We train a logistic regression algorithm to classify a whether a wine comes from Marlborough using:

1. An 80-20 train-test split.
2. Three features engineered from the description
3. 5-fold cross validation.

We report Kappa after using the model to predict provinces in the holdout sample.

```{.r}
# TODO
```


# 4. Binary vs Other Classification

What is the difference between determining some form of classification through logistic regression versus methods like $K$-NN and Naive Bayes which performed classifications.

> <span style="color:red;font-weight:bold">TODO</span>: *Explain.*


# 5. ROC Curves

We can display an ROC for the model to explain your model's quality.

```{.r}
# You can find a tutorial on ROC curves here: https://towardsdatascience.com/understanding-the-roc-curve-and-auc-dd4f9a192ecb/
```

> <span style="color:red;font-weight:bold">TODO</span>: *Explain.*