Description
1) Linear Regression (3 points)
Load the linearreg.csv file. This is the file you will use for this problem. There are two vectors in
the file X and Y. X consists of 30 instances of a univariate attribute vector, and Y is the response
vector. The intent of this problem is to get hands on experience doing polynomial regression (and
its limits) and to use cross-validation to get an idea of how model complexity relates to
bias/variance/MSE.
A. (2 point) Using n-fold cross-validation (the value of n is your choice, but must be explained in
your answer) with kth polynomial regression, fit a function to the data for values of k between 0
and 9. In your homework, show the plot of the mean square error on the validation set(s),
averaged over all of the folds, as a function of k. Also, plot the best function overlaying a
scatterplot of the data. The code for your work must be in a single file called nfoldpolyfit.py. The
stub for this file has been provided to you as an example. Below is the function signature, as well
as how it will be run from the command line
def nfoldpolyfit(X,Y,maxK,n, verbose)
python nfoldpolyfit.py
B. (0.5 points) Which value of k yielded the best results, in terms of accuracy of the prediction?
C. (0.5 point) Predict the response for a new query, x=3. Given the performance during crossvalidation, do you think this is an accurate prediction? Is there anything about the value 3 that
should give you pause, given the training data? Explain your reasoning.
2) Classification with Regression (2 points)
A. (1 point) Explain how to do classification via regression. Be clear. Use a graph to illustrate.
B. (1 point) Explain a weakness of classification via regression. Be clear. Use a graph and a
concrete example to illustrate.
EECS 349 (Machine Learning) Homework 3
3) Linear Discriminant Analysis (2 points)
Linear Discriminant Analysis (LDA) is a common technique for learning (you guessed it) a linear
discriminant. Look at chapter 4 of the Elements of Statistical Learning (that’s a free book, linked
to repeatedly from the course calendar). Look at the lecture slides (there may be more on the
slides than was covered in class). Now answer some questions about LDA.
A. (0.5 points) When there are 3 or more classes to be distinguished, what is a situation where
LDA will do a better job than classification via regression?
B. (0.5 points) What assumptions does LDA make about the data distribution(s)?
C.(0.5 points) What assumptions does Quadratic Discriminant Analysis (QDA) relax compared to
Linear Discriminant Analysis? What are the upsides and downsides of relaxing those
assumptions?
D.(0.5 points) How could I find a decision surface that could be modeled with a polynomial using
LDA, not QDA.
4) Perceptron Linear Discriminants (3 points)
Load the linearclass.csv file. There are four vectors in the file: X1, Y1, and X2, Y2. Vectors X1
and X2 both consist of 200 instances of a univariate attribute vector. Y1 and Y2 are the respective
output labels {-1,1} for each input vector, e.g. the k
th labeled instance for X1 is <X1(k),Y1(k)>.
begin initialize !, ! ← 0
do ! ← ! + 1 mod !
if !! is misclassified using !
then ! ← ! + !!!!
until all examples are properly classified
return !
end
Figure 1. Pseudo code for Sequential Perceptron Algorithm
Figure 1 contains pseudo code for the Sequential Perceptron Algorithm. Here…
w is the parameter vector (weight vector and threshold value)
m is the number of training examples
xk is the k
th training example
yk is the k
th training example class label {-1,1}
A. (1 point) Implement the sequential perceptron algorithm to learn the parameters for a linear
discriminant function that correctly assigns X1 to class -1 or 1. The algorithm should terminate
when the classification error is 0. Output the number of iterations of that the algorithm performed
before convergence and the learned parameters. Name your file perceptrona.py and include it
with your homework submission. Comment this code to the level you saw in the provided stub
for nfoldpolyfit.py Below is how the function will be run from the command line, as well as the
function signature. We have provided some starter code for reading in the csv file in
perceptrona.py
def perceptrona(w_init,X,Y):
#return a tuple (w, e)
return (w, e)
python perceptrona.py
EECS 349 (Machine Learning) Homework 3
where…
w is the parameter vector (weight vector and threshold value)
e is the number of epochs (one epoch is one iteration through all of X) the algorithm performed
w_init is the parameter vector (weight vector and threshold value)
X is the matrix of training examples (i.e. each row is an attribute vector with a prepended ‘1’ for
the threshold term)
Y is the column vector of class {-1,1} class labels for the training examples
B. (1 point) Using the same sequential perceptron algorithm from part A, learn the parameters for
a linear discriminant function that correctly assigns X2 to class -1 or 1. What happened and why?
C. (1 point) How can you transform the attributes of X2 so that your algorithm can correctly
classify the data? Add this transformation into your algorithm, and describe your changes. Name
your function (and file) “perceptronc(.py)” and include with your homework submission. This
function should have the exact same input and output parameters (in the same order) as
perceptrona.
