Summary
Summary DSCI Tutorial 9 - tutorial_regression2_solution (2022)
- Course
- Institution
Solutions to tutorial 9 Regression 2
[Show more]
1 review
By: ivyfeng • 10 months ago
Seller
Follow
travissmith1
Reviews received
Content preview
Tutorial 9: Regression ContinuedRegression learning objectives:
Recognize situations where a simple regression analysis would be appropriate for making predictions.
Explain the k-nearest neighbour (k-nn) regression algorithm and describe how it differs from k-nn classification.
Interpret the output of a k-nn regression.
In a dataset with two variables, perform k-nearest neighbour regression in R using tidymodels to predict the values for a test dataset.
Using R, execute cross-validation in R to choose the number of neighbours.
Using R, evaluate k-nn regression prediction accuracy using a test data set and an appropriate metric (e.g., root means square prediction error).
In a dataset with > 2 variables, perform k-nn regression in R using tidymodels to predict the values for a test dataset.
In the context of k-nn regression, compare and contrast goodness of fit and prediction properties (namely RMSE vs RMSPE).
Describe advantages and disadvantages of the k-nearest neighbour regression approach.
Perform ordinary least squares regression in R using tidymodels to predict the values for a test dataset.
Compare and contrast predictions obtained from k-nearest neighbour regression to those obtained using simple ordinary least squares regression
from the same dataset.
In R, overlay the ordinary least squares regression lines from geom_smooth on a single plot.
In [ ]:
### Run this cell before continuing.
library(tidyverse)
library(testthat)
library(digest)
library(repr)
library(tidymodels)
library(GGally)
library(ISLR)
options(repr.matrix.max.rows = 6)
source("tests.R")
source("cleanup.R")
Predicting credit card balance
Source: https://media.giphy.com/media/LCdPNT81vlv3y/giphy-downsized-large.gif (https://media.giphy.com/media/LCdPNT81vlv3y/giphy-downsized-
large.gif)
Here in this worksheet we will work with a simulated data set that contains information that we can use to create a model to predict customer credit card
balance. A bank might use such information to predict which customers might be the most profitable to lend to (customers who carry a balance, but do not
default, for example).
Specifically, we wish to build a model to predict credit card balance ( Balance column) based on income ( Income column) and credit rating ( Rating
column).
We access this data set by accessing it from an R data package that we loaded at the beginning of the worksheet, ISLR . Loading that package gives
access to a variety of data sets, including the Credit data set that we will be working with.
In [ ]:
Credit
, Question 1.1
{points: 1}
Select only the columns of data we are interested in using for our prediction (both the predictors and the response variable) and use the as_tibble
function to convert it to a tibble (it is currently a base R data frame). Name the modified data frame credit (using a lowercase c).
Note: We could alternatively just leave these variables in and use our recipe formula below to specify our predictors and response. But for this worksheet,
let's select the relevant columns first.
In [ ]:
### BEGIN SOLUTION
credit <- Credit %>%
select(Balance, Income, Rating) %>%
as_tibble()
### END SOLUTION
credit
In [ ]:
test_1.1()
Question 1.2
{points: 1}
Before we perform exploratory data analysis, we should create our training and testing data sets. First, split the credit data set. Use 60% of the data
and set the variables we want to predict as the strata argument. Assign your answer to an object called credit_split .
Assign your training data set to an object called credit_training and your testing data set to an object called credit_testing .
In [ ]:
set.seed(2000)
### BEGIN SOLUTION
credit_split <- initial_split(credit, prop = 0.6, strata = Balance)
credit_training <- training(credit_split)
credit_testing <- testing(credit_split)
### END SOLUTION
In [ ]:
test_1.2()
Question 1.3
{points: 1}
Using only the observations in the training data set, create a ggpairs scatterplot of all the columns we are interested in including in our model. Name
the plot object credit_eda .
In [ ]:
### BEGIN SOLUTION
options(repr.plot.height = 10, repr.plot.width = 15)
credit_eda <- credit_training %>%
ggpairs(mapping = aes(alpha = 0.4)) +
theme(text = element_text(size = 20))
### END SOLUTION
credit_eda
In [ ]:
test_1.3()
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller travissmith1. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $7.49. You're not tied to anything after your purchase.
Can Stuvia be trusted?
4.6 stars on Google & Trustpilot (+1000 reviews)
76710 documents were sold in the last 30 days
Founded in 2010, the go-to place to buy study notes for 14 years now