Description
Boston Data Set
In this assignment, we use Boston data set. This is a data set containing housing values in 506 suburbs of
Boston (a data frame with 506 rows and 13 variables).
• crim: per capita crime rate by town.
• zn: proportion of residential land zoned for lots over 25,000 sq.ft.
• indus: proportion of non-retail business acres per town.
• chas: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).
• nox: nitrogen oxides concentration (parts per 10 million).
• rm: average number of rooms per dwelling.
• age: proportion of owner-occupied units built prior to 1940.
• dis: weighted mean of distances to five Boston employment centres.
• rad: index of accessibility to radial highways.
• tax: full-value property-tax rate per $10,000.
• ptratio: pupil-teacher ratio by town.
• lstat: percent of households with low socioeconomic status.
• medv: median value of owner-occupied homes in $1000s.
set.seed(1)
library(ISLR2)
head(Boston)
## crim zn indus chas nox rm age dis rad tax ptratio lstat medv
## 1 0.00632 18 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 4.98 24.0
## 2 0.02731 0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 9.14 21.6
## 3 0.02729 0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 4.03 34.7
## 4 0.03237 0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 2.94 33.4
## 5 0.06905 0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 5.33 36.2
## 6 0.02985 0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 5.21 28.7
dim(Boston)
## [1] 506 13
Q1 (Deviance test, AIC, test MSE)
Our interest is to predict medv (median house value) using predictors
• rm(average number of rooms per house),
1
• age(proportion of owner-occupied units built prior to 1940) and
• lstat(percent of households with low socioeconomic status).
pairs(~ medv + rm + age + lstat, data = Boston)
medv
4 6 8
10 20 30 40 50
10 30
4 5 6 7 8
rm
age
0 20 40 60 80
10 20 30
10 30 50 0 40 80
lstat
We fit the following models:
m1 : medv ~ rm + lstat,
m2 : medv ~ rm + poly(lstat, df=2)
m3 : medv ~ rm + age + lstat
m4 : medv ~ rm + age + poly(lstat, df=2)
(a) (10 marks) Fit the model and use anova() function to do the deviance test to compare the models.
Choose the best model.
(b) (5 marks) Calculate AIC for each model fitted in (a). Choose the best model using the value of AIC.
(c) (10 marks) Split the data set (100%) into a training set (80%) and a test set (20%). Then fit model1–
model5 on the training set, and calculate the test MSE for each model. Choose the best model.
(d) (10 marks) By combining the result from (a), (b) and (c), decide the best model. Refit the chosen
model using all of the Boston data set. Make a prediction of medv for a suburb with values rm=10,
age=50 and lstat=10. Interpret the predicted value.
Q2 (LASSO, best subset selection)
We continue to work on Boston data set. The aim in Q2 is to predict medv (median house value) using all
predictors in Boston data set. In the following questions, we apply LASSO and the best subset selection
methods.
(a) (10 marks) (LASSO) Fit a lasso model on the training set, with λ chosen by cross-validation with the
1 se rule . Report the test error obtained, along with the values of non-zero coefficient estimates.
We use the training set and the test set created in Q1 (c).
(b) (10 marks) (Best subset selection) Do the best subset selection with BIC and choose the best model.
Report the values of coefficient estimates in the best model.
(c) (10 marks) Comparing the LASSO chosen model and the best subset selected model, which is the
better model? Explain why?
(d) (10 marks) How can you improve the fit of the best subset selected model?
[Total: 75 marks]