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CMPT 419/726: Assignment 1
Assignment1: Regression

1 Probabilistic Modeling and Bayes’ Rule
1. Assume the probability of being infected with Malaria disease is 0.01. The probability of
test positive given that a person is infected with Malaria is 0.95 and the probability of test
positive given the person is not infected with Malaria is 0.05.
(a) Calculate the probability of test positive.
(b) Use Bayes’ Rule to calculate the probability of being infected with Malaria given that
the test is positive.
2. Suppose P(rain today) = 0.30, P(rain tomorrow) = 0.60, P(rain today and tomorrow) = 0.25.
Given that it rains today, what is the probability it will rain tomorrow?
3. A biased die has the following probabilities of landing on each face:
face 1 2 3 4 5 6
P(face) 0.1 0.1 0.2 0.2 0 0.4
I win if the die shows odd. What is the probability that I win? Is this better or worse than a
fair die? (i.e., a die with equal probabilities for each face)?
2 Weighted Squared Error
The sum-of-squares error function for regression (Eqn. 3.12 in PRML) treats every training data
point equally. In some instances, we may wish to place different weights on different training
data points. This could arise if we have confidence estimates of the accuracy of each training data
point.
Consider the weighted sum-of-squares error function:
𝐸𝐷̂(𝑤) =
1
2
∑ 𝑎𝑛{𝑡𝑛 − 𝑤
𝑇𝜙(𝑥𝑛
)}
𝑁 2
𝑛=1
(1)
with weights 𝑎𝑛 0 on each training data point.
Derive the optimal weights w given this weighted sum-of-squares error function.
CMPT 419/726: Assignment 1 (Spring 2020) Instructor: Mo Chen
3
3 Training vs. Test Error
For the questions below, assume that error means RMS (root mean squared error).
1. Suppose we perform unregularized regression on a dataset. Is the validation error always
higher than the training error? Explain in 1-2 sentences.
2. 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 in 1-2 sentences.
3. Suppose we perform both regularized and unregularized regression on a dataset. Is the
testing error with a degree 20 polynomial always lower using regularized regression
compared to unregularized regression? Explain in 1-2 sentences.
4 Regression
In this question you will train models for regression and analyze a dataset. Start by downloading
the code and dataset from the website.
The data set 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 script polynomial_regression.py to load the dataset and names of countries /
features.
Answer the following questions about the data. Include these answers in your report.
1. Which country had the lowest child mortality rate in 1990? What was the rate?
2. Which country had the lowest child mortality rate in 2011? What was the rate?
3. Some countries are missing some features (see original .xlsx/.csv spreadsheet). How is
this handled in the function assignment1.load_unicef_data()?
CMPT 419/726: Assignment 1 (Spring 2020) Instructor: Mo Chen
4
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)1
.
• 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, 𝑥1
2
, 𝑥2
2
) and no cross-terms (𝑥1. 𝑥2).
Perform the following experiments:
1. Create a python script polynomial_regression.py for the following.
Fit a polynomial basis function regression (unregularized) for degree 1 to degree 6
polynomials. Include bias term. Plot training error and test error (in RMS error) versus
polynomial degree.
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
assignment1.normalize data().
Run the code again, and put this new plot in your report.
2. Create a python script polynomial_regression_1d.py 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). Try with and without a bias term.
Plot training error and test error (in RMS error) for each of the 8 features. This should be as
bar charts (e.g. use matplotlib.pyplot.bar()) — one for models with bias term, and
another for models without bias term.
Put the two bar charts in your report.
1 Zero-indexing, hence values[:,1].
CMPT 419/726: Assignment 1 (Spring 2020) Instructor: Mo Chen
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The testing 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.py 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. Create a python script sigmoid regression.py 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 = 2000.0. Include a bias term. Use unnormalized features.
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. Create a python script polynomial regression reg.py for the following.
Implement L2-regularized regression.
Fit a degree 2 polynomial using λ={0,.01,.1,1,10,102
,103
,104
}.
Use normalized features as input. Include a bias term. 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
matplotlib.pyplot.semilogx plot, putting λ on a log scale2
.
Put this plot in your report, and note which λ value you would choose from the cross
validation.
2 The 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.
CMPT 419/726: Assignment 1 (Spring 2020) Instructor: Mo Chen
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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.
(please take screenshots from your entire screen for the figures requested for question 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 appear3
. 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
./polynomial_regression_1d.py
the script produces the plots in your report from the relevant question.
3 This includes the data files and others which are provided as part of the assignment.

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