COMP5318/COMP4318 – Machine Learning and Data Mining Semester 1, 2023
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Assignment 1: Classification
Key information
Deadlines
Submission: 11:59pm, 6 April, 2023 (Thursday week 7, Sydney time)
Late submissions policy
Late submissions are allowed for up to 3 days late. A penalty of 5% per day late will apply. Assignments
more than 3 days late will not be accepted (i.e. will get 0 marks). The day cut-off time is 11:59pm.
Marking
This assignment is worth 15 marks = 15% of your final mark.
Your code will be marked for correctness. A few marks will be allocated for style – meaningful variable
names and comments.
We will run your code. If it doesn’t run, you will get 0 marks for the parts that don’t run.
The assignment should be completed in pairs (groups of 2 students). No more than 2 students are allowed.
Submission
This assignment must be written in Python in the Jupyter Notebook environment. A Jupyter Notebook
template is provided. Your implementation should use the same suite of libraries that we have used during
the tutorials, such as scikit-learn, numpy and pandas.
The assignment will be submitted in Canvas.
Submission instructions:
• Before you submit, you need to create a group. In Canvas -> “People”, select one of these two tabs:
“A1part1” or “A1part2”. Choose one of the empty groups listed and join it. Both you and your
partner must join the same group. Groups have a maximum of 2 members.
• When you are ready to submit your assignment, you need to submit it on behalf of the group in
the corresponding submission box. You need to submit two versions of your code: ipynb and pdf.
Only one student from the group needs to submit, not both.
• In summary:
o If you have registered your group under "A1part1", submit your ipynb code in "Submission:
Assignment 1 ipynb for A1part1 groups" and your pdf in "Submission: Assignment 1 pdf for
A1part1 groups"
o If you have registered your group under "A1part2", submit your ipynb code in
"Submission: Assignment 1 ipynb for A1part2 groups" and your pdf in "Submission:
Assignment 1 pdf for A1part2 groups"
It is important to follow the submission instructions carefully as otherwise your mark may not be recorded
correctly.
We had to create two options (A1part1 and A1part2) and two submission boxes because of the limitations
of Canvas for the number of groups it allows.
COMP5318/COMP4318 – Machine Learning and Data Mining Semester 1, 2023
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File names and student names
• The submission files should be named like this: a1-SID1-SID2.ipynb (.pdf), where SID1 and SID2 are
the SIDs of the two students
• In the Jupyter Notebook, include only your SIDs (as shown in the template) and not your name. The
marking is anonymous.
Task
In this assignment you will investigate a real dataset by implementing multiple classification algorithms.
You will first pre-process the dataset by replacing missing values and normalising the dataset with a min-
max scaler. You will then evaluate the performance of multiple classification algorithms: K-Nearest
Neighbour, Logistic Regression, Naïve Bayes, Decision Tree, Support Vector Machine, Bagging, AdaBoost,
Gradient Boosting and Random Forest, using the stratified 10-fold cross-validation method. You will also
apply a grid search to find the best parameters for some of these classifiers.
1. Data loading, pre-processing and printing
The dataset for this assignment is the Breast Cancer Wisconsin. It contains 699 examples described by 9
numeric attributes. There are two classes – class1, corresponding to benign breast cancer tumours, and
class2, corresponding to malignant breast cancer tumours. The features are computed from a digitized
image of a biopsy sample of breast tissue for a subject.
The dataset should be downloaded from Canvas: breast-cancer-wisconsin.csv. This file includes the
attribute (feature) headings and each row corresponds to one individual. Missing attributes in the dataset
are recorded with a ‘?’.
You will need to pre-process the dataset, before you can apply the classification algorithms. Three types of
pre-processing are required: filling in the missing values, normalisation and changing the class values. After
this is done, you need to print the first 10 rows of the pre-processed dataset.
1. Filling in the missing attribute values - The missing attribute values should be replaced with
the mean value of the column using sklearn.impute.SimpleImputer.
2. Normalising the data - Normalisation of each attribute should be performed using a min-max
scaler to normalise the values between [0,1] with sklearn.preprocessing.MinMaxScaler.
3. Changing the class values - The classes class1 and class2 should be changed to 0 and 1
respectively.
4. Print the first 10 rows of the pre-processed dataset. The feature values should be formatted to
4 decimal places using .4f, the class value is an integer. A function print_data has been provided
in the template to help you achieve this.

For example, if your normalised data looks like this:
Clump
Thickness
Uniformi
ty of Cell
Size
Uniformi
ty of Cell
Shape
Marginal
Adhesion
Single
Epithelial
Cell Size
Bare
Nuclei
Bland
Chromati
n
Normal
Nucleoli
Mitose
s
Class
0.1343 0.4333 0.5432 0.8589 0.3737 0.9485 0.4834 0.9456 0.4329 0
0.1345 0.4432 0.4567 0.4323 0.1111 0.3456 0.3213 0.8985 0.3456 1
0.4948 0.4798 0.2543 0.1876 0.9846 0.3345 0.4567 0.4983 0.2845 0

Then your program should print:
COMP5318/COMP4318 – Machine Learning and Data Mining Semester 1, 2023
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0.1343,0.4333,0.5432,0.8589,0.3737,0.9485,0.4834,0.9456,0.4329,0
0.1345,0.4432,0.4567,0.4323,0.1111,0.3456,0.3213,0.8985,0.3456,1
0.4948,0.4798,0.2543,0.1876,0.9846,0.3345,0.4567,0.4983,0.2845,0
(You need to print the first 10 rows not the first 3.)
Please note that we will test your code with another dataset, and your pre-processing should be written
with this in mind. See the “Marking Criteria” section for more detail.
2. Defining functions for the classification algorithms

Part 1: Cross-validation without parameter tuning
You will now apply multiple classifiers to the pre-processed dataset, in particular: Nearest Neighbor,
Logistic Regression, Naïve Bayes, Decision Tree, Bagging, Ada Boost and Gradient Boosting. All classifiers
should use the sklearn modules from the tutorials. All random states in the classifiers should be set to
random_state=0.
You need to evaluate the performance of these classifiers using 10-fold stratified cross-validation from
sklearn.model_selection.StratifiedKFold with these options:
cvKFold=StratifiedKFold(n_splits=10, shuffle=True, random_state=0)
You will need to pass cvKFold (the stratified folds) as an argument when calculating the cross-validation
accuracy, not cv=10 as in the tutorials. This ensures that random_state=0.
For each classifier, write a function that accepts the required input and returns the average cross-validation
score:
def exampleClassifier(X, y, [options]):
…
return scores.mean()
where X contains the attribute values and y contains the class (as in the tutorial exercises).
More specifically, the headers of the functions for the classifiers are given below:
Logistic Regression
def logregClassifier(X, y)
…
return scores.mean()
It should use LogisticRegression from sklearn.linear_model.
Naïve Bayes
def nbClassifier(X, y)
…
return scores.mean()
It should use GaussianNB from sklearn.naive_bayes
COMP5318/COMP4318 – Machine Learning and Data Mining Semester 1, 2023
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Decision Tree
def dtClassifier(X, y)
…
return scores.mean()
It should use DecisionTreeClassifier from sklearn.tree, with information gain (the entropy criterion)
Ensembles: Bagging, Ada Boost and Gradient Boosting
def bagDTClassifier(X, y, n_estimators, max_samples, max_depth)
…
return scores.mean()

def adaDTClassifier(X, y, n_estimators, learning_rate, max_depth)
…
return scores.mean()

def gbClassifier(X, y, n_estimators, learning_rate)
…
return scores.mean()

These functions should implement Bagging, Ada Boost and Gradient Boosting using BaggingClassifier,
AdaBoostClassifier and GradientBoostingClassifier from sklearn.ensemble. Bagging and
Ada Boost should combine decision trees and use information gain.

Part 2: Cross-validation with parameter tuning
For two other classifiers, SVM and Random Forest, we would like to find the best parameters using grid
search with 10-fold stratified cross-validation (GridSearchCV in sklearn).
The data should be split into training and test subsets using train_test_split from
sklearn.model_selection with stratification and random_state=0 (as in the tutorials but with
random_state=0).
You will need to pass cvKFold (the stratified folds) as an argument to GridSearchCV, not cv=10 as in the
tutorials. This ensures that random_state=0.
Write the following functions:
K-Nearest Neighbour
def bestKNNClassifier(X, y)
…
return (appropriate values so that the required printing can be done)
It should use the KNeighborsClassifier from sklearn.neighbors.
COMP5318/COMP4318 – Machine Learning and Data Mining Semester 1, 2023
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The grid search should consider the following values for the parameters k(n_neighbors) and p:
k = {1, 3, 5, 7, 9}
p = {1, 2}
The function should return appropriate values, so that best parameters found, the best cross-validation
accuracy and the test set accuracy can be printed when calling this function, see the next section.
SVM
def bestSVMClassifier(X,y)
…
return (appropriate values so that the required printing can be done)
It should use SVC from sklearn.svm with kernel set to ‘rbf’.
The grid search should consider the following values for the parameters C and gamma:
C = {0.01, 0.1, 1, 5, 15}
gamma = {0.01, 0.1, 1, 10, 50}
The function should return appropriate values, so that best parameters found, the best cross-validation
accuracy and the test set accuracy can be printed when calling this function, see the next section.
Random Forest
def bestRFClassifier(X,y)
It should use RandomForestClassifier from sklearn.ensemble with information gain and max_features
set to ‘sqrt’.
The grid search should consider the following values for the parameters n_estimators and max_leaf_nodes:
n_estimators = {10, 30, 60, 100, 150}
max_leaf_nodes = {6, 12, 18}
The function should return appropriate values, so that best parameters found, the best cross-validation
accuracy, the test set accuracy, the macro average F1 score, and the weighted average F1 score can be
printed when calling this function, see the next section.
3. Running the classifiers and printing the results
Run the classifiers from the previous section on the pre-processed dataset and print the results.
For Part 1 of this assignment, set the parameters as follows (this is already done for you in the template):

#Bagging
bag_n_estimators = 60
bag_max_samples = 100
bag_max_depth = 6

#AdaBoost
ada_n_estimators = 60
ada_learning_rate = 0.5
ada_bag_max_depth = 6
COMP5318/COMP4318 – Machine Learning and Data Mining Semester 1, 2023
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#GB
gb_n_estimators = 60
gb_learning_rate = 0.5
The printing should look like this but with the correct numbers (these are random numbers):
LR average cross-validation accuracy: 0.8123
NB average cross-validation accuracy: 0.7543
DT average cross-validation accuracy: 0.6345
Bagging average cross-validation accuracy: 0.8765
AdaBoost average cross-validation accuracy: 0.7165
GB average cross-validation accuracy: 0.9054


KNN best k: 7
KNN best p: 2
KNN cross-validation accuracy: 0.7853
KNN test set accuracy: 0.5991

SVM best C: 0.0100
SVM best gamma: 10.0000
SVM cross-validation accuracy: 0.8676
SVM test set accuracy: 0.8098

RF best n_estimators: 10
RF best max_leaf_nodes: 16
RF cross-validation accuracy: 0.8600
RF test set accuracy: 0.8321
RF test set macro average F1: 0.8123
RF test set weighted average F1: 0.8261
Format all numbers to 4 decimal places using .4f, except k, p, n_estimators and max_leaf_nodes which
should be formatted as integers.
Academic honesty – very important
Please read the University policy on Academic Honesty very carefully:
https://sydney.edu.au/students/academic-integrity.html
Plagiarism (copying from another student, website or other sources), making your work available to
another student to copy, engaging another person to complete the assignments instead of you (for
payment or not) are all examples of academic dishonesty. Note that when there is copying between
students, both students are penalised – the student who copies and the student who makes his/her work
available for copying
The University penalties are severe and include: 1) a permanent record of academic dishonesty on your
student file, 2) mark deduction, ranging from 0 for the assignment to Fail for the course and 3) expulsion
from the University and cancelling of your student visa.
If there is a suspected case, the investigation may take several months. Your mark will not be finalised
until the investigation is completed. This may create problems enrolling in other courses next semester
(COMP5318 is a pre-requisite for many courses) or delaying your graduation. Going through the
investigation is also very stressful.
COMP5318/COMP4318 – Machine Learning and Data Mining Semester 1, 2023
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In addition, the Australian Government passed a new legislation (Prohibiting Academic Cheating Services
Bill) that makes it a criminal offence to provide or advertise academic cheating services - the provision or
undertaking of work for students which forms a substantial part of a student’s assessment task.
Do not confuse legitimate co-operation and cheating! You can discuss the assignment with other students
but your group must write your own code.
We will use similarity detection software. If you cheat, the chances that you will be caught are very high.
Do not even think about engaging in plagiarism or academic dishonesty, it is not worth it. Be smart and
don’t risk your future by engaging in plagiarism and academic dishonesty!
Marking Criteria
The marking rubric is provided in Canvas.
Please note that we will test your program on another dataset. It will have the same format as the breast
cancer dataset but a different number of features and examples, and different names of the features. You
may assume the class value will be in the last column and there will be two classes as in the breast cancer
dataset. The missing values may be everywhere, not only in a single column as the breast cancer dataset.
Hence, do not hard-code the number of features and examples - do not set them to 699 and 9 as in the
breast cancer dataset, and do not make assumptions that the missing values will be in a column with a
specific name.
To test your code before submission, we have made available another dataset (test-before.csv), with the
correct results.