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CITS5508 Machine Learning

创建日期：2024-08-07 18:10:22

所属分类：计算机computer science

标签： CITS5508

Machine Learning

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CITS5508 Machine Learning

Assignment

Discussion is encouraged, but all work must be done and submitted individually. This assign- ment has 21 tasks, from which 20 are assessed, which total 45 marks.

You will develop Python code for classification tasks. You will use Grid Search and cross- validation to find the optimal hyperparameters of the model and discuss and interpret the differ- ent decisions and their impact on the model’s performance and interpretability.

1 Submission

Your submission consists of two files. The first file is a report describing your analysis/results. Your analysis should provide the requested plots, tables and your reflections about the results. Each deliverable task is indicated as D and a number. Your report should be submitted as a “.PDF” file. Name your file as assig1 <student id>.pdf (where you should replace <student id> with your student ID).

The second file is your Python notebook with the code supporting your analysis/results. Your code should be submitted as assig1 <student id>.ipynb, the Jupyter notebook exten- sion.

Submit your files to LMS before the due date and time. You can submit them multiple times. Only the latest version will be marked. Your submission will follow the rules provided in LMS.

Important:

You must submit the first part of your assignment as an electronic file in PDF format (do not send DOCX, ZIP or any other file format). Only PDF format is accepted, and any other file format will receive a zero mark.

You should provide comments on your code.

You must deliver parts one and two to have your assignment assessed. That is, your submission should contain your analysis and your Jupyter notebook with all coding, both with appropriate formatting.

Bysubmittingyourassignment,youacknowledgeyouhavereadallinstructionsprovided in this document and LMS.

There is a general FAQ section and a section in your LMS, Assignments - Assignment 2 - Updates, where you will find updates or clarifications about the tasks when necessary. It is your responsibility to check this page regularly.

You will be assessed on your thinking and process, not only on your results. A perfect performance without demonstrating understanding what you have done won’t provide you marks.

Your answer must be concise. A few sentences (2-5) should be enough to answer most of the open questions. You will be graded on thoughtfulness. If you are writing long answers, rethink what you are doing. Probably, it is the wrong path.

Youcanaskinthelaborduringconsultationifyouneedclarificationabouttheassignment questions.

You should be aware that some algorithms can take a while to run. A good approach to improving their speed in Python is to use the vectorised forms discussed in class. In this case, it is strongly recommended that you start your assignment soon to accommodate the computational time.

For the functions and tasks that require a random procedure (e.g. splitting the data into 80% training and 20% validation set), you should set the seed of the random generator to the value “5508” or the one(s) specified in the question.

2 Dataset

In this assignment, you are asked to train a few decision tree classifiers on the Breast cancer wisconsin (diagnostic) dataset available on Scikit-Learn and compare their performances.

Description about this dataset can be found on the Scikit-Learn web page:

https://scikit-learn.org/stable/datasets/toy dataset.html#breast-cancer-wisconsin-diagnostic-dataset

There are two classes in the dataset:

malignant (212 instances, class value 0) and

benign (357 instances, class value 1). Follow the example code given on the web page

https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load breast cancer.html#sklearn.datasets. load breast cancer

to read the dataset and separate it into a feature matrix and a class vector. Your feature matrix should have 569 (rows) × 30 (columns) and your class vector should have 569 elements.

In all asked implementations using Decision Trees, Random Forests or data splits (such as when using train test split()), you should set random state as specified for results reproducibil- ity. You should aim to round your results to the second decimal place.

3 Tasks

First inspections on the dataset and preprocessing

D1 2 marks

Re-order the columns in your feature matrix (or dataframe) based on the column name. Provide a scatter plot to inspect the relationship between the first 10 features in the dataset. Use different colours in the visualisation to show instances coming from each class. (Hint: use a grid plot.)

D2 2 marks

Provide a few comments about what you can observe from the scatter plot:

What can be observed regarding the relationship between these features?

Can you observe the presence of clusters of groups? How do they relate to the target variable?

Are there any instances that could be outliers?

Are there features that could be removed? Why or why not?

1 mark

Compute and show the correlation matrix where each cell contains the correlation coefficient between the corresponding pair of features (Hint: you may use the heatmap function from the seaborn package).

D4 1 mark

Do the correlation coefficients support your previous observations?

In a data science project, it’s crucial not just to remove highly correlated features but to consider the context and implications of feature selection carefully. Blindly dropping features may lead to the loss of valuable information or unintended bias in the model’s performance. Here, for the assignment context, we will drop a few features to simplify the classification tasks and speed up the computational time. Create a code that drop the features: mean perimeter, mean radius, worst radius, worst perimeter and radius error. These are features with a linear corre- lation higher than 0.97 in magnitude with some other features kept in the data.

After this process, your data matrix should be updated accordingly and contain 25 features. Task D5 must be performed; otherwise, your order deliverable tasks will be incorrect. However, there are no marks for task D5.

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