Supervised learning exercises

Introduction

These exercises are taken from the supervised learning chapter from Modern Data Science with R: http://mdsr-book.github.io. Other materials relevant for instructors (sample activities, overview video) for this chapter can be found there.

Sleep

The ability to get a good night’s sleep is correlated with many positive health outcomes. The NHANES data set in the NHANES package contains a binary variable SleepTrouble that indicates whether each person has trouble sleeping. For each of the following models:

1. Build a classifier for SleepTrouble
2. Report its effectiveness on the NHANES training data
3. Make an appropriate visualization of the model
4. Interpret the results. What have you learned about people’s sleeping habits?

You may use whatever variable you like, except for SleepHrsNight. Models:

• Null model
• Logistic regression
• Decision tree
• Random forest
• Neural network
• Naive Bayes
• K nearest neighbors

SOLUTION:

library(mdsr)   
library(NHANES)
# XX create test and train
# solution goes here

Quantitative sleep

Repeat the previous exercise, but now use the quantitative response variable SleepHrsNight. Build and interpret the following models:

• Null model
• Multiple regression
• Regression tree
• Random forest
• Ridge regression
• LASSO

SOLUTION:

library(mdsr)   
library(NHANES)
# XX create test and train
# solution goes here

Even more sleep

Repeat either of the previous exercises, but this time first separate the NHANES data set uniformly at random into 75% training and 25% testing sets. Compare the effectiveness of each model on training vs. testing data.

SOLUTION:

library(mdsr)   
library(NHANES)
# XX create test and train
# solution goes here

Pregnant?

Repeat the first exercise, but for the variable PregnantNow. What did you learn about who is pregnant?

SOLUTION:

library(mdsr)   
library(NHANES)
# XX create test and train
# solution goes here

NASA weather

The nasaweather package contains data about tropical storms from 1995-2005. Consider the scatterplot between the wind speed and pressure of these storms shown below.

<>= library(mdsr) library(nasaweather) ggplot(data = storms, aes(x = pressure, y = wind, color = type)) + geom_point(alpha = 0.5) @

The type of storm is present in the data, and four types are given: extratropical, hurricane, tropical depression, and tropical storm. There are complicated and not terribly precise definitions for storm type. Build a classifier for the type of each storm as a function of its wind speed and pressure.

Why would a decision tree make a particularly good classifier for these data? Visualize your classifier in the data space in a manner similar to Figure 8.10 or 8.11. % XX hardcoded ref!

SOLUTION:

library(mdsr)   
library(nasaweather)
# solution goes here

Arrival delays

Fit a series of supervised learning models to predict arrival delays for flights from New York to SFO using the nycflights13 package. How do the conclusions change from the multiple regression model presented in the Statistical Foundations Chapter?

SOLUTION:

library(mdsr)   
library(nasaweather)
# solution goes here

Use the College Scorecard Data (https://collegescorecard.ed.gov/data) to model student debt as a function of institutional characteristics using the techniques described in this chapter. Compare and contrast results from at least three methods. (Note that a considerable amount of data wrangling will be needed.)

SOLUTION:

library(mdsr)   
# solution goes here