1. Assignment-2_CS5710/Assignment-2_CS5710_Logistic-
Regression.pdf
Introduction to Machine Learning (CS 4710/5710)
Assignment-2 (80 points)
Logistic Regression
Due by 23rd march (Monday) 11:59pm
You are allowed to discuss the problem and solution design with
others, but the code you submit
must be your own. Your solution must include the certification
of authenticity “I certify that the
codes/answers of this assignment are entirely my own work.”
Datasets
The training and test files will follow the same format as the
text files in the UCI datasets.
Datasets and description of the datasets are uploaded with this
assignment onto Blackboard. For
each dataset, a training file and a test file are provided. The
name of each file indicates what
dataset the file belongs to, and whether the file contains

2. training or test data. Your code should
also work with ANY OTHER training and test files using the
same format as the files in the UCI
datasets.
Logistic Regression
You must implement a Python executable file called
logistic_regression that uses logistic
regression to fit a function to the given UCI datasets. Your code
should work for all the three
datasets (for different number of features). Your function
should be invoked as follows:
logistic_regression with following three command line
arguments: <training_file> <test_file>
• <training_file>: The first argument, <training_file> is the path
name of the training file,
where the training data is stored. The path name can specify any
file stored on the local
computer.
• <test_file>: The second argument, <test_file> is the path name
of the test file, where the
test data is stored. The path name can specify any file stored on
the local computer.

3. Training Stage for Logistic Regression
You need to apply gradient descent method at the training stage
of your model. The hypothesis
of the logistic regression is:
Gradient Descent Method:
At the end of the training stage, your program should print out
the values of the weights that you
have estimated. The output of the training phase should be a
sequence of lines like this:
�0=%.4f
�1=%.4f
�2=%.4f
...
Test Stage for Logistic Regression
After the training stage, you should apply the function that you
have learned on the test data. For

4. each test object (following the order in which each test object
appears in the test file), you should
print a line containing the following info:
• Object ID. This is the line number where that object occurs in
the test file.
• Predicted class (the result of the classification).
• True class (the last column on the line where the object
occurs).
• Accuracy. This is defined as follows:
o If the predicted class is correct, the accuracy is 1.
o If the predicted class is incorrect, the accuracy is 0.
The output of the test stage should be a sequence of lines like
this:
ID=%5d, output=%14.4f, target value = %10.4f,
Misclassification error = %4d
Submission Guidelines and Requirements
• Please zip up logistic_regression.py (or
logistic_regression.ipynb). Submit the zip file via

5. Blackboard.
• Include your name, UCM ID and Certification statement in
your solution:
//Your name
// Your UCM ID
//Certificate of Authenticity: “I certify that the codes/answers of
this assignment are
entirely my own work.”
Assignment-2_CS5710/UCI_Dataset/Description of UCI
Datasets.docx
Description of UCI Datasets
The files in the UCI datasets directory contain training files and
test files for three datasets. Both the training file and the test
file are text files, containing data in tabular format. Each value
is a number, and values are separated by white space. The i-th
row and j-th column contain the value for the j-th dimension of
the i-th object. The only exception is the LAST column, that
stores the class label for each object. Make sure you do not use
data from the last column (i.e., the class labels) as parts of the
input vector.
The datasets are copied from the UCI repository of machine
learning datasets. Here are some details on each dataset:
· The pendigits dataset. This dataset contains data for pen-based
recognition of handwritten digits.
· 7494 training objects.
· 3498 test objets.
· 16 dimensions.
· 10 classes.

6. · The satellite dataset. The full name of this dataset is Statlog
(Landsat Satellite) Data Set, and it contains data for
classification of pixels in satellite images.
· 4435 training objects.
· 2000 test objets.
· 36 dimensions.
· 6 classes.
· The yeast dataset. This dataset contains some biological data
· 1000 training objects.
· 484 test objets.
· 8 dimensions.
· 10 classes.
For each dataset, a training file and a test file are provided. The
name of each file indicates what dataset the file belongs to, and
whether the file contains training or test data.
Note that, for the purposes of your assignments, it does not
matter at all where the data come from. The methods that you
are asked to implement should work on all three datasets, as
well as ANY other datasets following the same format.
Assignment-2_CS5710/UCI_Dataset/pendigits_test.txt
88 92 2 99 16 66 94 37 70 0 0 24 42 65 100 100 8
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7. 3 71 0 95 45 100 100 99 79 78 48 53 31 24 54 0 7
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10. 88 90 18 100 17 66 79 33 49 0 0 28 41 67 100 98 8
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11. 0 63 37 44 42 6 1 0 25 36 65 67 100 100 46 80 8
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12. 76 67 75 100 0 77 49 40 83 0 5 12 36 52 100 87 8
34 100 14 83 2 66 0 48 23 34 65 30 91 17 100 0 4
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13. 23 89 59 100 75 68 58 33 36 0 0 18 48 27 100 32 7
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14. 18 73 62 100 53 85 53 53 52 20 0 0 8 6 100 6 1
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19. 64 65 100 100 41 85 67 42 64 0 0 16 20 59 81 74 8
0 54 47 78 69 100 53 68 33 35 0 6 31 0 100 7 1
34 99 9 67 52 42 35 0 0 4 19 62 52 97 100 100 5
48 73 27 86 0 48 7 5 53 0 91 27 100 71 66 100 0
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20. 73 100 24 85 0 39 22 0 70 20 100 62 78 99 31 76 0
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21. 0 84 54 100 100 79 95 42 69 8 29 0 28 26 90 30 7
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23. 77 93 35 77 83 37 72 0 0 18 37 60 100 94 75 100 8
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30 73 81 71 82 100 33 91 81 68 100 35 55 9 0 0 9
35 100 27 68 0 37 65 42 100 60 100 66 96 32 96 0 4
100 100 57 85 20 57 0 27 26 0 90 8 81 34 14 36 6
84 100 55 81 32 58 20 33 30 9 100 6 52 13 0 0 6
17 86 68 100 71 70 57 60 100 41 91 7 35 0 0 19 3

24. 99 89 26 100 27 68 83 32 40 0 0 27 63 61 100 91 8
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48 84 99 100 100 94 53 74 48 44 90 26 55 0 0 1 5
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100 81 54 100 0 72 43 36 66 0 26 24 67 61 95 91 8
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14 85 54 100 100 96 72 72 87 41 92 10 47 0 0 3 3

25. 17 100 21 85 0 53 45 47 96 57 92 61 98 28 100 0 4
0 95 41 100 59 67 51 27 40 0 12 32 52 47 100 55 7
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47 100 31 76 0 45 63 50 100 64 92 61 94 28 96 0 4
14 90 65 100 100 84 57 65 71 53 91 26 56 3 0 0 3

26. 0 91 25 63 86 33 75 0 14 11 53 43 100 75 75 100 8
53 100 16 78 0 47 59 38 100 63 86 58 58 29 29 0 4
24 61 45 100 90 99 68 54 29 17 0 14 51 0 100 5 2
20 82 71 100 100 96 41 87 10 71 62 48 54 0 0 0 5
100 91 75 100 47 79 39 45 73 40 78 13 41 0 0 0 5
18 75 51 100 65 72 41 35 8 3 0 0 50 6 100 15 2
13 96 0 71 78 61 82 12 7 0 4 49 15 97 100 100 5
74 100 47 84 0 66 23 53 84 42 100 44 60 23 33 0 4
0 67 44 80 88 100 100 88 88 66 69 44 50 22 50 0 1
100 77 64 61 9 74 54 100 100 85 91 51 59 19 0 0 9
72 74 63 100 0 78 67 42 100 0 15 4 38 47 …
For your information, you need to implement Multi-Class
(multi-nomial) Logistic Regression using one-vs-all method in
Assignment-2. Please see the lecture slides for more
information on one-vs-all method. All three datasets of UCI
Datasets have more than two classes, so datasets are applicable
for Multi-Class Logistic Regression. Another reminder (as
announced several times in the class): please do not use any
library functions (other than Numpy and Pandas) for your
implementation. Please let me know if you have any questions.
Thank you.