Description
1 Probabilistic Modeling (12 marks)
In lecture we went over an example of modeling coin tossing – estimating a parameter that is the
probability the coin comes up heads.
Consider instead the problem of modeling the outcome of the Canadian Federal election. To simplify matters, assume one party will win a majority (i.e. either the NDP, Liberals, Conservatives,
or Green Party wins).
1. (4 marks) What is the type of distribution that describes this situation? What are the parameters µ of this distribution? (See PRML Appendix B)
2. (2 marks) What would be the value of the parameters µ for an election where the outcome is
an equal chance of each party winning?
3. (2 marks) What would be the value of the parameters µ for an election that is completely
“rigged”? E.g. the party currently in power is definitely going to win.
4. (4 marks) Suppose my prior is that the Green Party has completely rigged the election.
Assume I see a set of polls where the NDP has the largest share of the vote in each poll.
What would be my posterior probability on the parameters µ?
2 Regularized Least-Squares Linear Regression (15 marks)
Show that the minimizer for least-squares linear regression with L2 regularization is w =
λI + Φ
T Φ
−1
Φ
T
t.
3 Training vs. Test Error (12 marks)
1. (4 marks) Suppose we perform unregularized regression on a dataset. Is the training error
with a degree 10 polynomial always lower than or equal to that using a degree 9 polynomial?
Explain.
2. (4 marks) Suppose we perform unregularized regression on a dataset. Is the testing error
with a degree 10 polynomial always lower than or equal to that using a degree 9 polynomial?
Explain.
3. (4 marks) Suppose we perform unregularized regression on a dataset. Is the training error
always lower than the testing error? Explain.
CMPT 419/726: Assignment 1 Instructor: Greg Mori
4 Regression (70 marks)
In this question you will train models for regression and analyze a dataset. Start by downloading
the code and dataset from the website.
The dataset is created from data provided by UNICEF’s State of the World’s Children 2013 report:
http://www.unicef.org/sowc2013/statistics.html
Child mortality rates (number of children who die before age 5, per 1000 live births) for 195
countries, and a set of other indicators are included.
4.1 Getting started
Run the provided function loadUnicefData.m to load the dataset and names of countries /
features.
Answer the following questions about the data. Include these answers in your report.
1. (2 marks) Which country had the highest child mortality rate in 1990? What was the rate?
2. (2 marks) Which country had the highest child mortality rate in 2011? What was the rate?
3. (2 marks) Some countries are missing some features (see original .xlsx/.csv spreadsheet).
How is this handled in the function loadUnicefData.m?
For the rest of this question use the following data and splits for train/test and cross-validation.
• Target value: column 2 (Under-5 mortality rate (U5MR) 2011).
• Input features: columns 8-40.
• Training data: countries 1-100 (Afghanistan to Luxembourg).
• Testing data: countries 101-195 (Madagascar to Zimbabwe).
• Cross-validation: subdivide training data into folds with countries 1-10 (Afghanistan to Austria), 11-20 (Azerbaijan to Bhutan), … . I.e. train on countries 11-100, validate on 1-10; train on
1-10 and 21-100, validate on 11-20, …
4.2 Polynomial Regression
Implement linear basis function regression with polynomial basis functions. Use only monomials
of a single variable (x1, x2
1
, x2
2
) and no cross-terms (x1 · x2). You may find the provided function
designMatrix.m useful.
Perform the following experiments:
1. (20 marks) Create a MATLAB script polynomial regression.m for the following.
Fit a polynomial basis function regression (unregularized) for degree 1 to degree 6 polynomials. Plot training error and test error (in RMS error) versus polynomial degree.
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CMPT 419/726: Assignment 1 Instructor: Greg Mori
Put this plot in your report, along with a brief comment about what is “wrong” in your report.
Normalize the input features before using them (not the targets, just the inputs x). Use
normalizeData.m.
Run the code again, and put this new plot in your report.
2. (20 marks) Create a MATLAB script polynomial regression 1d.m for the following.
Perform regression using just a single input feature.
Try features 8-15 (Total population – Low birthweight). For each (un-normalized) feature fit
a degree 3 polynomial (unregularized).
Plot training error and test error (in RMS error) for each of the 8 features. This should be a
bar chart (use bar([train err test err])).
Put this bar chart in your report.
The training error for feature 11 (GNI per capita) is very high. To see what happened,
produce plots of the training data points, learned polynomial, and test data points. The code
visualize 1d.m may be useful.
In your report, include plots of the fits for degree 3 polynomials for features 11 (GNI), 12
(Life expectancy), 13 (literacy).
4.3 Sigmoid Basis Functions
1. (10 marks) Create a MATLAB script sigmoid regression.m for the following.
Implement regression using sigmoid basis functions for a single input feature. Use two
sigmoid basis functions, with µ = 100, 10000 and s = 2000. Include a bias term.
Fit this regression model using feature 11 (GNI per capita).
In your report, include a plot of the fit for feature 11 (GNI).
In your report, include the training and testing error for this regression model.
4.4 Regularized Polynomial Regression
1. (20 marks) Create a MATLAB script polynomial regression reg.m for the following.
Implement L2-regularized regression. Fit a degree 2 polynomial using λ = {0, .01, .1, 1, 10, 102
, 103
, 104}.
Use 10-fold cross-validation to decide on the best value for λ. Produce a plot of average validation set error versus λ. Use a semilogx plot, putting λ on a log scale1
.
Put this plot in your report, and note which λ value you would choose from the crossvalidation.
1The unregularized result will not appear on this scale. You can either add it as a separate horizontal line as a
baseline, or report this number separately.
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CMPT 419/726: Assignment 1 Instructor: Greg Mori
Submitting Your Assignment
The assignment must be submitted online at https://courses.cs.sfu.ca. In order to
simplify grading, you must adhere to the following structure.
You must submit two files:
1. You must create an assignment report in PDF format, called report.pdf. This report
must contain the solutions to questions 1-3 as well as the figures / explanations requested for
4.
2. You must submit a .zip file of all your code, called code.zip. This must contain a single
directory called code (no sub-directories, no leading path names), in which all of your files
must appear2
. There must be the 4 scripts with the specific names referred to in Question 4,
as well as a common codebase you create and name.
As a check, if one runs
unzip code.zip
cd code
matlab
polynomial_regression_1d
the script produces the plots in your report from the relevant question.
2This includes the data files and others which are provided as part of the assignment.
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