Description
1. For data (x, y) with a joint distribution p(x, y) = p(y|x)p(x), the expected loss of a function f(x) to
model y using loss function l(f(x), y) is given by
E [l(f(x), y)] = Z
x
Z
y
l(f(x), y)p(y|x)
p(x)dx
(a) (7 points) What is the optimal f(x) when l(f(x), y) = (f(x) − y)
2
(b) (8 points) What is the optimal f(x) when l(f(x), y) = |f(x) − y)|, where |.| represents absolute
value.
2. (15 Points) For data with joint distribution p(y, x) with y an M-valued discrete target variable. Show
that the error rate of the Bayes optimal classifier for class y = Cj is given by:
err [y = Cj ] = X
i=1:M
Z
x∈Ri
p(Cj |x)p(x)dx −
Z
x∈Rj
p(Cj |x)p(x)dx
where Rk is the region of x where p(yk|x) > p(yi
|x)∀i =6 k
Please show all your work! Answers to the first two problems without supporting work
will not be given credit.
Programming assignments: The next two problems involve programming. We will be considering
two datasets for these assignments:
(a) Boston Housing: The Boston housing dataset comes prepackaged with scikit-learn. The dataset
has 506 points, 13 features, and 1 target (response) variable. You can find more information about
the dataset here:
http://www.cs.toronto.edu/ delve/data/boston/bostonDetail.html
Where the first 13 variables are attributes, and the median home value (variable 14) is the target.
from s kl e a r n . d a t a s e t s import l o a d b o s t o n
b o s t on = l o a d b o s t o n ( )
While the original dataset is for a regression problem, we will create two classification datasets
for the homework. Note that you only need to modify the target t to create these classification
datasets, and the attribute data X should not be changed.
i. Target = HomeVal50 Create a binary target variable by thresholding at the 50th percentile of
the target scores. Note by construction the proportions of the two classes are equal (π = 1/2).
(b) Handwritten Digits This is a version of the classic handwritten digit dataset. The dataset has
1797 points, 64 features, and 10 classes corresponding to ten numbers 0, 1, . . . , 9.
from s kl e a r n . d a t a s e t s import l o a d d i g i t s
d i g i t s = l o a d d i g i t s ( )
1
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Besides loading from scikit, these two datasets are also provided on Moodle course page
in csv format. The last column shows target values. A target value is either discrete
(e.g., class label) or continuous (e.g., house price) and it is what we are trying to predict
or classify. All other columns are data values. Each row is an input point.
3. (30 points) Use Fishers linear discriminant analysis (LDA) for this problem. The problem will require
you to explain, and implement the key equations for the discrimant directions ~wk, answer questions
about these equations and use the key equations from Gaussian generative modeling to code a classifier.
Prediction error of the resulting classifier needs to be assessed by training and evaluating the following
classifiers using 10-fold cross-validation:
(a) (10 points) For the HomeVal50 dataset, apply LDA in the general case, i.e., both SB and SW
computed from the data, to project data to R (one dimension). Histogram the projected points.
Are the data well-modeled by a linear discriminant? Justify your answer carefully, simple yes or
no yields no credit.
(b) (5 points) Using LDA, can you project the Boston50 data to R2 (two dimensions)? Clearly justify
your answer with technical details. (A simple yes/no answer will receive zero credit.)
(c) (15 points) For the Digits dataset, apply LDA in the general case, i.e., both SB and SW computed
from the data, to project data to R2
(two dimensions) by using two eigenvectors, followed by bivariate Gaussian generative modeling to do 10-class classification, i.e., by estimating and using
class priors πk and parameters (µk, Ck, k = 1, . . . , 10.
You will have to submit:
i. Summary of Methods and Results Briefly describe the approaches in (a), (b) and (c)
above, along with relevant equations. Also, report the training and test set error rates and
standard deviations from 10-fold cross validation for the methods on the dataset in part (c).
ii. Code for each algorithm implemented: For part (a), you will have to submit code for
LDA1dProjection(filename,num crossval)(main file). This main file has input: a filename
including extension (exactly the same as your downloaded file) and absolute path (e.g., in
Unix: /home/mywork/boston.csv). The content of input data must be exactly the same as
downloaded file, or load from scikit (Juypter notebook) and generates: a plot of the histograms
of projected points, separately for both classes (20 bins).
For part (b), you will have to submit code for LDA2dGaussGM(filename,numcrossval), with
inputs: (1) filename as before (2) the number of folds for cross-validation, and output: (1)
the training and test set error rates and standard deviations printed to the terminal (stdout).
4. (40 points) The goal is compare simple generative and discriminative classifiers on the learning curves
using the HomeVal50 and Digits datasets. For background, see “On Discriminative vs. Generative
Classifiers: A comparison of logistic regression and naive Bayes” by A. Ng and M. Jordan, available in
moodle. You will train and evaluate two classifiers on both datasets:
(a) (20 points) Logistic regression (LR), and
(b) (20 points) Naive-Bayes with marginal Gaussian distributions (GNB)
(a) Evaluation Evaluation will be done using 10 random class-specific 80-20 train-test splits, i.e., for
each class, pick 80% of the data at random for training, train a classifier using training data from
all classes, use the remaining 20% of the data from each class as testing, and repeat this process
10 times. We will be doing a learning curve, similar to the Ng-Jordan paper.
(b) Code For logistic regression, you will have to implement the Iterately Reweighted Least Squares
algorithm as a function with the form
l o g i s t i c R e g r e s s i o n ( fil e n am e , num s p l i t s , t r a i n p e r c e n t )
This main file has input: (1) a filename including extension, (2) the number of 80-20 train-test
splits for evaluation, (3) and a vector containing percentages of training data to be used for
Cont.
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training (use [10 25 50 75 100] for the plots), and output: (1) test set error rates for each training
set percent printed to the terminal (stdout). The test set error rates should include both the error
rates for each split for each training set percentage as well as the mean of the test set error rates
across all splits for each training set percentage (print the mean error rates at the end).
For naive Bayes, you will have to submit code for
n aiveB aye sG au s si an ( fil e n am e , num s p l i t s , t r a i n p e r c e n t )
that implements feature wise generative Gaussian Modeling, with all other guidelines staying the
same.
(c) Plots: Your plots will be based on 10 random 80-20 train-test splits. For each split, we will
always evaluate results on the same test set (20% of the data), while using increasing percentages
of the training set (80% of the data) for training. In particular, we will use the following training
set percentages: [10 25 50 75 100], so that for each 80-20 split, we use 10%, 25%, all the way up
to 100% of the training set for training, and always report results on the same test set. We will
repeat the process 10 times, and plot the mean and standard deviation (as error bars) of the test
set errors as a function of training set percentage.
(d) Summary: You will have to submit a summary of methods and results
i. Briefly describe the approaches for Logistic Regression and Naive Bayes, including key equations modeling the data log likelihood and the (iterative) equations for parameter estimation.
ii. For each dataset and method, create a plot of the test set error rate illustrating the relative
performance of the two methods with increasing number of training points. The plots will
be similar in spirit to Figure 1 in the Ng-Jordan paper, along with error-bars with standard
deviation of the errors.
Additional instructions: Code can only be written in Python or Matlab; no other programming languages
will be accepted. One should be able to execute all programs from matlab/python command prompt or the
code can be a jupyter notebook. Your code must be runnable on a CSE lab machine.
Please specify instructions on how to run your program in the README file. Information on the size of
the datasets, including number of data points and dimensionality of features, as well as number of classes
can be readily extracted from the dataset text file. Each function must take the inputs in the order specified
in the problem and display the textual output via the terminal. The input data file for your function must
be exactly the same as the original downloaded file, which will be used as input for grading. For each part,
you can submit additional files/functions (as needed) which will be used by the main file.
For this assignment note whether you are allowed to use scikit code. If indicated, you cannot
use machine learning libraries such as those available from scikit-learn for learning the models or for crossvalidation. However, you may always use libraries for basic matrix computations. Put comments in your
code so that one can follow the key parts and steps in your code.
Cont.
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Submission Instructions:
Follow the rules strictly. If we cannot run your code, you will not get any credit.
Things to submit:
1. hw1.pdf: A document which contains the solution to Problems 1, 2, 3, and 4
which including the summary of methods and results.
2. LDA1dProject: Code for Problem 3.
3. logisticRegression and naiveBayesGauss: Code for Problem 4.
4. README.txt: README file that contains your name, student ID, email, instructions on how to compile (if necessary) and run your code, any assumptions
you are making, and any other necessary details.
5. Any other files, except the data, which are necessary for your code.
The End.
