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# STAT 435 Homework # 3 solution

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STAT 435
Homework # 3

Online Submission Via Canvas
Instructions: You may discuss the homework problems in small groups, but you
must write up the final solutions and code yourself. Please turn in your code for the
problems that involve coding. However, for the problems that involve coding, you
must also provide written answers: you will receive no credit if you submit code without written answers. You might want to use Rmarkdown to prepare your assignment.

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STAT 435
Homework # 3

Online Submission Via Canvas
Instructions: You may discuss the homework problems in small groups, but you
must write up the final solutions and code yourself. Please turn in your code for the
problems that involve coding. However, for the problems that involve coding, you
must also provide written answers: you will receive no credit if you submit code without written answers. You might want to use Rmarkdown to prepare your assignment.
1. A random variable X has an Exponential(λ) distribution if its probability density function is of the form
f(x) = (
λe−λx if x 0
0 if x ≤ 0
,
where λ 0 is a parameter. Furthermore, the mean of an Exponential(λ)
random variable is 1/λ.
Now, consider a classification problem with K = 2 classes and a single feature
X ∈ R. If an observation is in class 1 (i.e. Y = 1) then X ∼ Exponential(λ1).
And if an observation is in class 2 (i.e. Y = 2) then X ∼ Exponential(λ2). Let
π1 denote the probability that an observation is in class 1, and let π2 = 1 − π1.
(a) Derive an expression for Pr(Y = 1 | X = x). Your answer should be in
terms of x, λ1, λ2, π1, π2.
(b) Write a simple expression for the Bayes classifier decision boundary, i.e.,
an expression for the set of x such that Pr(Y = 1 | X = x) = Pr(Y = 2 |
X = x).
(c) For part (c) only, suppose λ1 = 2, λ2 = 7, π1 = 0.5. Make a plot of
feature space. Clearly label:
i. the region of feature space corresponding to the Bayes classifier decision boundary,
ii. the region of feature space for which the Bayes classifier will assign
an observation to class 1,
1
iii. the region of feature space for which the Bayes classifier will assign
an observation to class 2.
(d) Now suppose that we observe n independent training observations,
(x1, y1), . . . ,(xn, yn).
Provide simple estimators for λ1, λ2, π1, π2, in terms of the training
observations.
(e) Given a test observation X = x0, provide an estimate of
P(Y = 1 | X = x0).
Your answer should be written only in terms of the n training observations
(x1, y1), . . . ,(xn, yn), and the test observation x0, and not in terms of any
unknown parameters.
2. We collect some data for students in a statistics class, with predictors X1 =
number of lectures attended, X2 = average number of hours studied per week,
and response Y = receive an A. We fit a logistic regression model, and get
coefficient estimates βˆ
0, βˆ
1, βˆ
2.
(a) Write out an expression for the probability that a student gets an A, as a
function of the number of lectures she attended, and the average number
of hours she studied per week. Your answer should be written in terms of
X1, X2, βˆ
0, βˆ
1, βˆ
2.
(b) Write out an expression for the minimum number of hours a student should
study per week in order to have at least an 80% chance of getting an A.
0, βˆ
1, βˆ
2.
(c) Based on a student’s value of X1 and X2, her predicted probability of
getting an A in this course is 60%. If she increases her studying by one
hour per week, then what will be her predicted probability of getting an
A in this course?
3. When the number of features p is large, there tends to be a deterioration in
the performance of K-nearest neighbors (KNN) and other approaches that
perform prediction using only observations that are near the test observation
for which a prediction must be made. This phenomenon is known as the curse
of dimensionality. We will now investigate this curse.
(a) Suppose that we have a set of observations, each with measurements on
p = 1 feature, X. We assume that X is uniformly distributed on [0, 1].
Associated with each observation is a response value. Suppose that we
wish to predict a test observation’s response using only observations that
are within 10% of the range of X closest to that test observation. For
instance, in order to predict the response for a test observation with X =
0.6, we will use observations in the range [0.55, 0.65]. On average, what
fraction of the available observations will we use to make the prediction?
2
(b) Now suppose that we have a set of observations, each with measurements
on p = 2 features, X1 and X2. We assume that (X1, X2) are uniformly distributed on [0, 1] × [0, 1]. We wish to predict a test observation’s response
using only observations that are within 10% of the range of X1 and within
10% of the range of X2 closest to that test observation. For instance, in
order to predict the response for a test observation with X1 = 0.6 and
X2 = 0.35, we will use observations in the range [0.55, 0.65] for X1 and
in the range [0.3, 0.4] for X2. On average, what fraction of the available
observations will we use to make the prediction?
(c) Now suppose that we have a set of observations on p = 100 features. Again
the observations are uniformly distributed on each feature, and again each
feature ranges in value from 0 to 1. We wish to predict a test observation’s response using observations within the 10% of each feature’s range
that is closest to that test observation. What fraction of the available
observations will we use to make the prediction?
(d) Using your answers to parts (a)-(c), argue that a drawback of KNN when
p is large is that there are very few training observations “near” any given
test observation.
(e) Now suppose that we wish to make a prediction for a test observation by
creating a p-dimensional hypercube centered around the test observation
that contains, on average, 10% of the training observations. For p = 1, 2,
and 100, what is the length of each side of the hypercube? Comment on
Note: A hypercube is a generalization of a cube to an arbitrary number
of dimensions. When p = 1, a hypercube is simply a line segment, when
p = 2 it is a square.
4. Pick a data set of your choice. It can be chosen from the ISLR package (but
not one of the data sets explored in the Chapter 4 lab, please!), or it can
be another data set that you choose. Choose a binary qualitative variable in
your data set to be the response, Y . (By binary qualitative variable, I mean
a qualitative variable with K = 2 classes.) If your data set doesn’t have any
binary qualitative variables, then you can create one (e.g. by dichotomizing
a continuous variable: create a new variable that equals 1 or 0 depending on
whether the continuous variable takes on values above or below its median). I
suggest selecting a data set with n ? p.
(a) Describe the data. What are the values of n and p? What are you trying
to predict, i.e. what is the meaning of Y ? What is the meaning of the
features?
(b) Split the data into a training set and a test set. Perform LDA on the
training set in order to predict Y using the features. What is the training
error of the model obtained? what is the test error?
3
(c) Perform QDA on the training set in order to predict Y using the features.
What is the training error of the model obtained? what is the test error?
(d) Perform logistic regression on the training set in order to predict Y using
the features. What is the training error of the model obtained? what is
the test error?
(e) Perform KNN on the training set in order to predict Y using the features.
What is the training error of the model obtained? what is the test error?