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COMPSCI 361: Machine Learning
创建日期：2024-05-09 17:08:06
标签： COMPSCI 361
Machine Learning

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COMPSCI 361: Machine Learning

Assignment 3: Group Project (Worth 10% in Total)
General Instruction:
Recall that we have covered a series of supervised learning approaches, in particular,
classification in our lectures, including Logistic Regression (week 5), Naive Bayes (week 5),
SVM (week 8-9), and Neural Networks (week 10). The goal of this assignment is to investigate
supervised learning algorithms for article classification on BBC news datasets using LR, NB,
SVM, NNs. This assignment can be divided into four parts. Please write a python program to
complete the following tasks using the Scikit-learn library:
● Task 1: Exploratory data analytics on text data
● Task 2: Perform classification models (LR, NB, SVM, NNs) to build article classifiers
using the given dataset.
● Task 3: Investigate the impact of multiple hyperparameters. Compare the classification
quality across four classification models in terms of F1 measure. Learn to manage
overfitting or underfitting situations.
● Task 4: Report your answers for each question and summarize your insights
Datasets:
Let’s consider two classes of BBC news articles: tech news and entertainment news. After
loading the data (.csv file) using pandas library, you will see that each news article is a comma-
separated line with three columns: news ID, processed news body, news class. The
processed news bodies are tokenized and lower-cased with removal of stop words and special
characters. You can find the two data files, train.csv and test.csv, in A3.zip on Canvas.
Submission:
Each group leader submits a single report (“Your_Tutorial_Group_Name.pdf" or in .HTML)
and the source code with detailed comments (“Your_Tutorial_Group_Name.py" or in
.ipynb) on Canvas by 23:59, Friday 27 May 2022. Your report should be no more than five
pages. Each group member shall submit a peer review form (Your_UPI.pdf), including
contribution weights of your own and the rest of the group members. The total contribution
weights of your own and the rest of the group members should be added up to 1. Individual
marks will be adjusted according to final weighting. You may submit to Canvas many times.
Penalty Dates:
The assignment will not be accepted after the last penalty date unless there are special
circumstances (e.g., sickness with medical certificate, family/personal emergencies). Penalties
will be calculated as follows as a percentage of the mark for the assignment.
● By 23:59, Saturday 28 May 2022 (10% penalty)
● By 23:59, Sunday 29 May 2022 (30% penalty)
Task 1: Exploratory Data Analytics [1 pts]
(a) Load the dataset and construct a feature vector for each article in the entire dataset. You
need to report the number of articles, and the number of extracted features. Show 5 example
articles with their extracted features using a dataframe. [0.5 pts]
(b) Conduct term frequency analysis and report three plots: (i) top-50 term frequency distribution
across the entire dataset, (ii) term frequency distribution for respective class of articles, and (iii)
class distribution. [0.5 pts]
Task 2: Classification Models Learning [4 pts]
(a) LR. Train your logistic regression classifier with L2-regularization. Consider different values
of the regularization term λ. Describe the effect of the regularization parameter λ on the outcome
in terms of bias and variance. Report the plot generated for specific λ values with training loss
on the y-axis versus λ on the x-axis to support your claim. [1 pts]
(b) NB. Train a Naive Bayes classifier using all articles features. Report the (i) top-20 most
identifiable words that are most likely to occur in the articles over two classes using your NB
classifier, and (ii) the top-20 words that maximize the following quantity . Which list of
(

=1|=)
(

=1|≠)
words describe the two classes better? Briefly explain your reasoning. [1 pts]
(c) SVM. Train your SVM classification models on the training dataset. You need to report two
surface plots for: (i) the soft-margin linear SVM with your choice of misclassification penalty ( ),
and (ii) the hard-margin RBF kernel with your choice of kernel width ( ). Explain the impact ofσ
penalty on the soft-margin decision boundaries, as well as the kernel hyperparameter on the
hard-margin decision boundaries. [1 pts]
(d) NN. Consider the neural network with the following hyperparameters: the initial weights
uniformly drawn in range [0,0.1] with learning rate 0.01.
● Train a single hidden layer neural network using the hyperparameters on the training
dataset, except for the number of hidden units (x) which should vary among 5, 20, and
40. Run the optimization for 100 epochs each time. Namely, the input layer consists of n
features x = [x1, ..., xn]T , the hidden layer has x nodes z = [z1, ..., zx]T , and the output
layer is a probability distribution y = [y1, y2]T over two classes.
● Plot the average training cross-entropy loss as shown below on the y-axis versus the
number of hidden units on the x-axis. Explain the effect of numbers of hidden units.
[1 pts] =−
=1
2
∑

log(

^
)
Task 3: Classification Quality Evaluation [4 pts]
(a) We explore how the size of the training data set affects the test and train accuracy. For each
value of m in [0.1, 0.3, 0.5, 0.7, 0.9], train your classifier on the first m portion of the training
examples (that is, use the data given by XTrain[0:mN] and yTrain[0:mN]). Please report two
plots: (i) training and (ii) testing accuracy for each such value of m with the x-axis referring to m
and the y-axis referring to the classification accuracy in measure as shown below. In total,1
there should be four curves for training accuracy and four curves for testing accuracy. Explain
the general trend of the two plots in terms of training and testing accuracy if any.
1 = 2 · × +
[2 pts]
(b) Let’s use 5-fold cross-validation to assess model performance. Investigate the impact of key
hyperparameters of your choices for each classifier using a testing dataset. Take SVM as an
example, the classification accuracy may be significantly affected by the kernels and
hyperparameter combination. List hyperparameters for each classifier and demonstrate how
these hyperparameters impact on the testing accuracy. [1 pts]
(c) Report and compare your LR, NB, SVM, and NN classifiers with the best hyperparameter
settings. Summarize what you have observed in the classification accuracy in measure on1
the testing dataset. [1 pts]
Task 4: Report Writing [1 pts]
If you use jupyter notebook for this assignment, you may consider to export your notebook as
an .HTML file and submit HTML and ipynb to Canvas.
Collaboration Policy:
● You are encouraged to do the group projects as a tutorial group of 5~6 people. Group
members are responsible for dividing up the work equally and making sure that each
member contributes. Please inform me early for mediations if you encounter any troubles
in the group setting.
● The purpose of student collaboration is to facilitate learning. You are encouraged to seek
help from each other in understanding the material needed to solve a particular
homework problem. If you encounter difficulties working in group projects, feel free to
consult with any of the instructors or tutors.
● Once your group lead submits the group project report and code, feedback will be
provided along with the markings.
● You are encouraged to consult tutors or instructors early on your project if you intend to
conduct studies beyond the project scope.
● All consultations can take place via (i) Piazza, (ii) office hours, or (iii) email.
Grading Rubric:
Task 1(a) 1 mark for the correct output.
Task 1(b) 1 mark for the correct output.
Task 2 (a) 1 mark for the correct implementation of LR and requested output.
Task 2 (b) 1 mark for the correct implementation of NB and requested output.
Task 2 (c) 1 mark for the correct implementation of SVM and requested output.
Task 2 (d) 1 mark for the correct implementation of NN and requested output.
Task 3 (a) 1 mark for correct requested output format. 1 mark for the observations according
to two requested plots with varying sizes of training data.
Task 3 (b) 1 mark for correct requested output format and the discussion of how chosen
hyperparameters impact the testing accuracy.
Task 3 (c) 1 mark for discussion of comparative testing accuracy across four classifiers.
Task 4 1 mark for clarity of the report and clarity of the comments.
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