Compare regression methods

C S 487/519 Applied Machine Learning
Compare regression methods
1 Objective
In this individual homework, you are required to understand and compare several regression algorithms.
2 Requirements
2.1 Tasks
(1) (60 points) Write code to conduct regression by
(a) (35 points) utilizing several regression functions: (i) LinearRegression, (ii) RANSACRegressor,
(iii) Ridge, and (iv) Lasso. These functions are provided by the Python scikit-learn library.
(b) (25 points) using one approach to conduct non-linear regression, and
(2) (20 points) Each regressor needs to be tested using the California housing dataset, which can be loaded
using fetch_california_housing from sklearn.datasets. You need to use all the columns and all
the instances in this dataset. During lectures, I may just use one column and a subset of the dataset
for demonstration purpose. If you decide to use fewer columns/instances of the dataset, you need to
show an analysis of why you chose to use only a subset of features/instances. For example, you can
provide a correlation analysis to justify why you are not using all columns, or show a random sample
of the instances. If you do not justify why you are using fewer columns, points will be deducted.
(3) (15 points) Properly analyze the regressors’ behavior by applying the knowledge that we discussed in
class. Such analysis should include at least Mean squared error (MSE) (or R2 score, or residual plots)
and running time. Put the analysis to report.pdf file.
(4) (5 points) Write a readme file readme.txt with detailed instructions to run your program.
2.2 Other requirements
• Your Python code should be written for Python version 3.5.2 or higher.
• Please write proper comments in your code to help the instructor and teaching assistants to understand it.
• Please properly organize your Python code (e.g., create proper classes, modules).
• You can put your code to Jupyter Notebook or a .py file.
3 Submission instructions
Put all your files (Python code, readme file, report, etc.) to a zip file named hw.zip and upload it to Canvas.
4 Grading criteria
(1) ZERO point will be given if your code does not work. Please do not submit code that
you did not test and make sure it works.
(2) The score allocation has been put beside the questions.
(3) FIVE points will be deducted if files are not submitted in the required format.
(4) If the total points are more than 100. Your grades will be scaled to the range of [0,100].
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(5) Please make sure that you test your code thoroughly by considering all possible test cases. For this
homework, your code will NOT be tested using more datasets. Thus, it does not need to be flexible to
accept different datasets as input.
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