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Assignment 3 – Linear Regression

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CS 383 – Machine Learning
Assignment 3 – Linear Regression
Introduction
In this assignment you will explore gradient descent and perform linear regression on a dataset using
cross-validation to analyze your results.
As with all homeworks, you cannot use any functions that are against the “spirit” of the assignment.
For this assignment that would mean an linear regression functions. You may use statistical and
linear algebra functions to do things like:
• mean
• std
• cov
• inverse
• matrix multiplication
• transpose
• etc…
And as always your code should work on any dataset that has the same general form as the
provided one.
Grading
Although all assignments will be weighed equally in computing your homework grade, below is the
grading rubric we will use for this assignment:
Part 1 (Theory) 15pts
Part 2 (Gradient Descent) 20pts
Part 3 (Closed-form LR) 40pts
Part 4 (S-Folds LR) 15pts
Report 10pts
TOTAL 100
Table 1: Grading Rubric
1
Datasets
Fish Length Dataset (x06Simple.csv) This dataset consists of 44 rows of data each of the form:
1. Index
2. Age (days)
3. Temperature of Water (degrees Celsius)
4. Length of Fish
The first row of the data contains header information.
Data obtained from: http://people.sc.fsu.edu/ jburkardt/datasets/regression/regression.html
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1 Theory
1. (10pts) Consider the following data:
















−2 1
−5 −4
−3 1
0 3
−8 11
−2 5
1 0
5 −1
−1 −3
6 1
















(a) Compute the coefficients for the linear regression using least squares estimate (LSE), where
the second value (column) is the dependent variable (the value to be predicted) and the
first column is the sole feature. Show your work and remember to add a bias feature and
to standardize the features. Compute this model using all of the data (don’t worry about
separating into training and testing sets).
2. For the function g(x) = (x − 1)4
, where x is a single value (not a vector or matrix):
(a) What is the gradient with respect to x? Show your work to support your answer.
(b) What is the global minima for g(x)? Show your work to support your answer.
(c) Plot x vs g(x) use a software package of your choosing.
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2 Gradient Descent
In this section we want to visualize the gradient descent process on the function g(x) = (x−1)4
. You
should have already derived (pun?) the gradient of this function in the theory section. To bootstrap
the process, initialize x = 0 and terminate the process when the change in the x from one iteration
to another is less that 2−23
.
2.1 Fixed Learning Rate
First experiment with your choice of the learning parameter, η. From your theory work you should
know what the actual minima is. A common starting guess is η = 1.0.
In your report you will need
1. Plot iteration vs g(x).
2. Plot iteration vs x.
3. Chosen value of η.
2.2 Adaptive Learning Rate
Next let’s try to “intelligently” adapt the learning rate. Start with η = 1.0 and reduce η by 1/2
whenever the sign of the gradient changes. This indicates that we may have over-jumped the minima.
In your report you will need
1. Plot iteration vs g(x).
2. Plot iteration vs x.
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3 Closed Form Linear Regression
Download the dataset x06Simple.csv from Blackboard. This dataset has header information in its
first row and then all subsequent rows are in the format:
ROW Id, Xi,1, Xi,2, Yi
Your code should work on any CSV data set that has the first column be header information, the
first column be some integer index, then D columns of real-valued features, and then ending with a
target value.
Write a script that:
1. Reads in the data, ignoring the first row (header) and first column (index).
2. Randomizes the data
3. Selects the first 2/3 (round up) of the data for training and the remaining for testing
4. Standardizes the data (except for the last column of course) using the training data
5. Computes the closed-form solution of linear regression
6. Applies the solution to the testing samples
7. Computes the root mean squared error (RMSE): q
1
N
PN
i=1(Yi − Yˆ
i)
2
. where Yˆ
i
is the predicted
value for observation Xi
.
Implementation Details
1. Seed the random number generate with zero prior to randomizing the data
2. Don’t forget to add in the bias feature!
In your report you will need:
1. The final model in the form y = θ0 + θ1×1 + …
2. The root mean squared error.
RMSE: Around 800
Table 2: Closed Form Regression Evaluation
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4 S-Folds Cross-Validation
Cross-Validation is a technique used to get reliable evaluation results when we don’t have that much
data (and it is therefore difficult to train and/or test a model reliably).
In this section you will do S-Folds Cross-Validation for a few different values of S. For each run you
will divide your data up into S parts (folds) and test S different models using S-Folds Cross-Validation
and evaluate via root mean squared error. In addition, to observe the affect of system variance, we
will repeat these experiments several times (shuffling the data each time prior to creating the folds).
We will again be doing our experiment on the provided fish dataset.
Write a script that:
1. Reads in the data, ignoring the first row (header) and first column (index).
2. 20 times does the following:
(a) Randomizes the data
(b) Creates S folds.
(c) For i = 1 to S
i. Select fold i as your testing data and the remaining (S −1) folds as your training data
ii. Standardizes the data (except for the last column of course) based on the training
data
iii. Train a closed-form linear regression model
iv. Compute the squared error for each sample in the current testing fold
(d) You should now have N squared errors. Compute the RMSE for these.
3. You should now have 20 RMSE values. Compute the mean and standard deviation of these.
The former should give us a better “overall” mean, whereas the latter should give us feel for
the variance of the models that were created.
Implementation Details
1. Don’t forget to add in the bias feature!
2. Set your seed value at the very beginning of your script (if you set it within the 20 tests, each
test will have the same randomly shuffled data!).
In your report you will need:
1. The average and standard deviation of the root mean squared error for S = 3 over the 20
different seed values..
2. The average and standard deviation of the root mean squared error for S = 5 over the 20
different seed values.
3. The average and standard deviation of the root mean squared error for S = 20 over 20 different
seed values.
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4. The average and standard deviation of the root mean squared error for S = N (where N is
the number of samples) over 20 different seed values. This is basically leave-one-out crossvalidation.
S Average RMSE Std of RMSE
3 650 45
5 650 35
20 620 10
N 620 0
Table 3: Evaluation Using S-Fold Cross Validation
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Submission
For your submission, upload to Blackboard a single zip file with no spaces in the file or directory
names and contains:
1. PDF Writeup
2. Source Code
3. readme.txt file
The readme.txt file should contain information on how to run your code to reproduce results for each
part of the assignment.
The PDF document should contain the following:
1. Part 1:
(a) Your solutions to the theory question
2. Part 2:
(a) Your two figures using η value of your choosing, as well as that value.
(b) Your two figures with an adaptive η.
3. Part 3:
(a) Final Model
(b) RMSE
4. Part 4:
(a) Average and standard deviations of RMSEs for different cross-validations.
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