Introduction

The wine dataset contains the results of a chemical analysis of wines from the same area of Italy, but with grapes from two different cultivars. The results of 13 chemical analyses recorded for each sample are provided in the dataset, and your goal is to create a classifier using some of these features to classify the type of wine (A vs. B).

This dataset is modified and based on data made available by Lichman, M. (2013). http://archive.ics.uci.edu/ml. Irvine, CA: University of California, School of Information and Computer Science.

Getting started

Clone your assignment repo into RStudio Cloud and open the R Markdown file. Don’t forget to load in the necessary packages and configure git:

library(tidyverse)
library(class)
library(broom)
library(usethis)
use_git_config(user.name="your name", user.email="your email")

If you would like your git password cached for a week for this project, type the following in the Terminal:

git config --global credential.helper 'cache --timeout 604800'

You will need to enter your GitHub username and password one more time after caching the password.This is only good for this single RStudio Cloud project; you will need to cache your credentials for each project you create.

Data

To get started, read in the data and save it as an object named wine.

wine <- read_csv("data/wine.csv")

Exercises

Write all R code according to the style guidelines discussed in class. Be especially careful about staying within the 80 character limit. Finally, make sure all code is fully reproducible (e.g., do not “hard-code” in values).

All team members must commit and push to receive full credit.

In addition to glm(), your code should only contain functions from the loaded R packages, unless an exercise states otherwise.

For this lab, we are going to focus on the following predictors for wine cultivar (type, coded as A or B):

Variable name	Description
`alcohol`	Alcohol content, in ABV
`malic`	Malic acid content; malic acid is one of the main flavor compounds in wine
`ash`	Ash content; the leftover inorganic matter after evaporation and incineration
`phenols`	Total phenol content; phenols are also responsible for affecting the taste of wine
`color`	Color intensity; how dark the wine is

Creating training and testing datasets

First, let’s set aside some data for training our models on, and some data for testing our models. Run the following code to create a vector of row indices to set aside as our test data. Note that we have set a random seed to ensure reproducibility of our results

set.seed(03052020)
indices <- sample(nrow(wine), 25) # 25 observations to test on

Create two new datasets, wine_train and wine_test. wine_train should only contain observations that ARE NOT in the row indices created above, and wine_test should only contain observations that ARE in the row indices created above. Be careful about what variables are included in the datasets you create. Finally, create a vector of the class labels for the training dataset and call it train_type.

k-nearest neighbors

When using function knn(), set argument use.all to FALSE.

In this case, we know the true wine types in our test dataset. Create a vector of these true wine types and call it true_type.
Fit the k-nearest neighbors model on our raw training dataset, using k = 10. What percent of our test data was accurately classified? As a hint, consider the following code and output:
```
x <- c("A", "B", "C", "A", "B", "C")
y <- c("A", "A", "C", "A", "B", "H")
x == y
mean(x == y)
```
Repeat Exercise 3, but with k varying from 1 to 12. Which value of k results in the greatest prediction accuracy in our test dataset, and what is the associated prediction accuracy? In doing this exercise, do not “repeat” the same code twelve times.

Hint #1: you may use the which.max() function, which returns the position of the maximum value of a vector.

Hint #2: Consider the following code, which creates an empty vector of twelve observations:
```
acc_pc <- numeric(12)
```
Hint #3: The tibble() function can be used to coerce a vector into a tibble.
As stated in class, a potential problem for k-NN is that all predictors must be on the same measurement scale, which is not the case for our current dataset. Create new variables in the original wine dataset that consist of standardized values for the predictors of interest. To create a standardized value, subtract the sample mean of the variable from each observation, and then divide this by the sample standard deviation of the variable. This gives us an estimate of how many standard deviations away from the mean each observation is. Overwrite the wine dataset with these additional variables so they can be used for later analyses.
Create wine_train_std and wine_test_std as new training and testing datasets that contain only these new standardized predictors. Fit the k-NN model on the training dataset, with k varying from 1 to 12. Which value of k results in the greatest prediction accuracy in our new testing dataset, and what was the prediction accuracy? Comment on your findings.

Logistic regression

The R function glm() requires for logistic regression that the response variable takes on values of 0 or 1. Create a new variable in the training dataset named bin_type that is 0 if the wine was type A, and 1 if the wine was type B.

Hint: Remember to use tidyverse syntax, for instance using if_else() or case_when().
Create a logistic regression model for the estimated probability of being type B wine, based on the original (unstandardized) predictors as found in the training dataset wine_train. Interpret each coefficient.
Calculate the predicted probabilities of being type B wine for each one of the 25 individuals in the test dataset based on the logistic regression model, and then classify them, using 0.5 as the decision boundary. Display the predictions (type A vs. B). What is the prediction accuracy?

Submission

Knit to PDF to create a PDF document. Stage and commit all remaining changes, and push your work to GitHub. Make sure all files are updated on your GitHub repo.

Please only upload your PDF document to Gradescope. Associate the “Overall” graded section with the first page of your PDF, and mark where each answer is to the exercises. If any answer spans multiple pages, then mark all pages.

Only one team member needs to submit for the group. After you hit submit, go to View or edit group and select all your team members from the drop-down menu.

Lab 07 - Classification

Due: Friday, Mar 27 at 11:59pm EST