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DTS304TC Machine Learning
创建日期：2024-11-05 15:32:00
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Assessment Task 1

                                                                              XJTLU Entrepreneur College (Taicang) Cover Sheet

Module code and Title 
 DTS304TC Machine Learning 
School Title
 School of AI and Advanced Computing 
Assignment Title
 Assessment Task 1 
Submission Deadline
 23:59, 24th March (Sunday), 2024 (China Time, GMT + 8) 
Final Word Count
 N/A 
If you agree to let the university use your work anonymously for teaching and learning purposes, please type “yes” here. 

I certify that I have read and understood the University’s Policy for dealing with Plagiarism, Collusion and the Fabrication of Data (available on Learning Mall Online). With reference to this policy I certify that:
⚫ My work does not contain any instances of plagiarism and/or collusion. My work does not contain any fabricated data. 
By uploading my assignment onto Learning Mall Online, I formally declare that all of the above information is true to the best of my knowledge and belief.  
Moreover, an external test set without ground truth labels has been provided. Your classifier's performance will be evaluated based on this set, underscoring the importance of building a model with strong generalization capabilities. The competencies you develop during this practical project are not only essential for successfully completing this assessment but are also highly valuable for your future pursuits in the field of data science.
Throughout this project, you are encouraged to utilize code that was covered during our Lab sessions, as well as other online resources for assistance. Please ensure that you provide proper citations and links to any external resources you employ in your work. However, the use of Generative AI for content generation (such as ChatGPT) is not permitted on all assessed coursework in this module.  
Submission deadline: TBD Percentage in final mark: 50% Learning outcomes assessed:
A. Demonstrate a solid understanding of the theoretical issues related to problems that machine learning algorithms try to address. B. Demonstrate understanding of properties of existing ML algorithms and new ones. C. Apply ML algorithms for specific problems.
Individual/Group: Individual
Length: The assessment has a total of 4 questions which gives 100 marks. The submitted file must be in pdf format.
Late policy: 5% of the total marks available for the assessment shall be deducted from the assessment mark for each working day after the submission date, up to a maximum of five working days Risks:
• Please read the coursework instructions and requirements carefully. Not following these instructions and requirements may result in loss of marks.

• The formal procedure for submitting coursework at XJTLU is strictly followed. Submission link on Learning Mall will be provided in due course. The submission timestamp on Learning Mall will be used to check late submission.  


Question 1: Coding Exercise - Heart Disease Classification with Machine Learning (50 Marks)

In this coding assessment, you are presented with the challenge of analyzing a dataset that contains patient demographics and health indicators to predict heart disease classifications. This entails solving a multi-class classification problem with five distinct categories, incorporating both categorical and numerical attributes. Your initial task is to demonstrate proficiency in encoding categorical features and imputing missing values to prepare the dataset for training a basic classifier. Beyond these foundational techniques, you are invited to showcase your advanced skills. This may include hyperparameter tuning using sophisticated algorithms like the Asynchronous Successive Halving Algorithm (ASHA). You are also encouraged to implement strategies for outlier detection and handling, model ensembling, and addressing class imbalance to enhance your model's performance. 


Project Steps:
a) Feature Preprocessing (8 Marks)
⚫ You are required to demonstrate four key preprocessing steps: loading the dataset, encoding categorical features, handling missing values, and dividing the dataset into training, validation, and test sets.
⚫ It is crucial to consistently apply the same feature preprocessing steps—including encoding categorical features, handling missing values, and any other additional preprocessing or custom modifications you implement—across the training, validation, internal testing, and the externally provided testing datasets. For efficient processing, you may want to consider utilizing the sklearn.pipeline and sklearn.preprocessing library functions. b) Training Classifiers (10 Marks)
⚫ Train a logistic regression classifier with parameter tuned using grid search, and a random forest classifier with parameters tuned using Async Successive Halving Algorithm (ASHA) with ray[tune] libraries. You should optimize the model's AUC score during the hyperparameter tuning process.
⚫ You should aim to optimize a composite score, which is the average of the classification accuracy and the macro-averaged F1 score. This objective encourages a balance between achieving high accuracy overall and ensuring that the classifier performs well across all classes in a balanced manner, which is especially important in multi-class classification scenarios where class imbalance might be a concern. To clarify, your optimization goal is to maximize a composite accuracy metric defined as follows: accuracy = 0.5 * (f1_score(gt, pred, average='macro') + accuracy_score(gt, pred)) In this formula, f1_score and accuracy_score refer to functions provided by the scikit-learn library, with f1_score being calculated with the 'macro' average to treat all classes equally.
⚫ Ensure that you perform model adjustments, including hyperparameter tuning, on the validation set rather than the testing set to promote the best generalization of your model.
⚫ We have included an illustrative example of how to implement the ASHA using the ray[tune] library. Please refer to the notebook DTS304TC_ASHA_with_Ray_Tune_Example.ipynb located in our project data folder for details. c) Additional Tweaking and External Test Set Benchmark (19 Marks) 



⚫ You are encouraged to explore a variety of advanced techniques to improve your model's predictive power. 1. Utilizing different classifiers, for example, XGBoost. 2. Implementing methods for outlier detection and treatment. 3. Creating model ensembles with varied validation splits. 4. Addressing issues of class imbalance. 5. Applying feature engineering strategies, such as creating composite attributes. 6. Implementing alternative validation splitting strategies, like cross-validation or stratified sampling, to enhance model tuning. 7. Additional innovative and valid methods not previously discussed.
You will be awarded 3 marks for successfully applying any one of these methods. Should you incorporate two or more of the aforementioned techniques, a total of 6 marks will be awarded.
Please include code comments that explain how you have implemented these additional techniques. Your code and accompanying commentary should explicitly state the rationale behind the selection of these supplementary strategies, as well as the underlying principles guiding your implementation. Moreover, it should detail any changes in performance, including improvements, if any, resulting from the application of these strategies. An additional 4 marks will be awarded for a clear and comprehensive explanation. To facilitate a streamlined review and grading process, please ensure that your comments and relevant code are placed within a separate code block in your Jupyter notebook, in a manner that is readily accessible for our evaluation.
⚫ Additionally, utilize the entire dataset and the previously determined optimal hyperparameters and classification pipeline to retrain your top-performing classifier. Then, apply this model to the features in 'dts304tc_a1_heart_disease_dataset_external_test.csv', which lacks true labels, to produce a set of predictive probability scores. Save these probabilistic scores in a table with two columns: the first column for patient IDs and the second for the output classification labels. Export this table to a file named external_test_results_[your_student_id].csv. Submit this file for evaluation. In the external evaluation conducted by us, your scores will be benchmarked against the performance of models developed by your classmates. You will receive four marks for successfully completing the prescribed classifier retraining and submission process. Additionally, your classifier's benchmark ranking—based on its performance relative to models developed by your peers—will be assigned five marks, contingent upon your standing in the ranking.  

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