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CITS5508 Machine Learning
Semester 1, 2020
Lab Sheet 3
Assessed, worth 10%. Due: 11:59pm, Monday 20th April 2020
1 Outline
This lab sheet consists of two small projects. The first project asks you to train an ensemble classifier
(Voting classifier) and report its performance in terms of its F1 score on the test set. The second project
asks you to train and test two Random Forest classifiers. This lab sheet is a good practical exercise to
test your understanding of the techniques covered in Chapters 6–7.
2 Submission
Name your Jupyter Notebook files as lab03proj1.ipynb and lab03proj2.ipynb for the two projects
below and submit them to LMS before the due date and time shown above. You can submit your files
multiple times. Only the latest version will be marked.
3 Project 1
Your tasks for this project is to train and evaluate the performance of a Voting classifier in classifying
the Cellular Localization Sites of Proteins on some E. Coli bacteria data. Your Voting classifier should
comprise three individual classifiers: an SVM classifier, a Logistic Regression classifier, and a Gradient
Descent classifier.
The data file and a brief description file are available from a web repository managed by the UCI
(University of California at Irvine) Centre for Machine Learning and Intelligent Systems. See
https://archive.ics.uci.edu/ml/datasets/ecoli
You should be able to find the data in the ecoli.data1 file and description about data in the ecoli.names
file. You should save the data file in the same directory as your Jupyter Notebook file. Although the data
file name does not end with “.csv”, you should be able to read it in using the read csv function as
usual. You will need to hard code the column headings in your Python code as they are not in the data
file. The data file has nine columns. The first eight columns are the features and the last column is the
class label. There are 336 instances in total.
• You will need to think about what to do with non-numerical data. Out of the eight classes, you
need to remove those classes having less than 10 instances as it is not possible to classify them.
Provide some plots for data visualization after you have successfully read in the data.
• Perform an 80/20 split of the data to form your training set and test set. Once this is done, the same
training set and test set should be used for all the classifiers.
• You will need to consider whether any data normalization is needed. Please describe your data
normalization step in your markdown cell(s). There is no need to use the Pipeline class. Note
that data normalization should be performed on the training set and test set separately.
1If you use the MacOS, the operating system may automatically rename the downloaded file as ecoli.data.txt. You will need
to rename the file back to ecoli.data. Otherwise, your code would not work when we mark it.
1
• There is no need to do grid search for finding optimal hyperparameters for each classifier. You
should be able to get reasonably good classification results for this dataset even using the default
hyperparameter values. Based on the settings of these hyperparameters, you can then make small
adjustment to see if the performance can be improved or not.
• For all the individual classifiers and the Voting classifier, report their classification accuracies,
F1 scores, and confusion matrices on the test set. For each confusion matrix, you are required
to display it in a diagram. You can use the plot confusion matrix function given by the
author (see the ipynb file for Chapter 3) to show each confusion matrix.
Hints:
• When calling the function read csv, there is an optional argument that you can use to specify
the character that separates the columns of the data.
• The data cleaning process for this project involves removing some data instances. You can use
various functions, such as loc and drop, from the pandas.DataFrame package to remove
rows and columns of the DataFrame object. However, even after some rows are correctly re-
moved, the index locations of the remaining rows in the DataFrame object are not automatically
updated. This means that you would still be able to access the removed rows and you would see
NAN (meaning not a number, i.e., undefined) for those rows. To overcome this problem, you will
need to explicitly call the reset index function to renumber the index locations. After per-
forming the data split, you should check that you don’t have any NAN values in both the training
and test sets.
4 Project 2
In the UCI Machine Learning Repository website above, there is a Parkinsons dataset:
http://archive.ics.uci.edu/ml/datasets/Parkinsons
which has 23 attributes and two class labels: “healthy” and “unhealthy” (i.e., having the Parkinsons
disease). The class labels appear under the status column. The data (as a text file in csv format) and
description about the attributes can be downloaded, respectively, from
http://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/parkinsons.data
http://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/parkinsons.names
Same as project 1, you should save the data file in the same directory as your Jupyter Notebook file.
• Consider performing appropriate data cleaning. Are there any columns that should be removed?
Perform this step in Python and provide some explanation.
• Divide the data into training and test sets (using 80/20 split). Investigate whether data normaliza-
tion is essential for the Random Forest classifier. You may need to iterate the step in the next bullet
point and the step here a few times to find out.
• Implement two Random Forest classifiers with different hyperparameter values. Report and com-
pare the F1 scores of the two classifiers.
5 Penalty on late submissions
See the URL below about late submission of assignments:
https://ipoint.uwa.edu.au/app/answers/detail/a id/2711/∼/consequences-for-late-assignment-submission

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