Description
TASK (20 points)
You are a new hire in a mid-size company. You have just completed your first task. Today
morning, you handed your report in and now you circle around the office. You feel exhausted
and a bit nervous. You don’t really know what to expect next.
After the lunch, you were
approached by your technical manager. “Our boss wants to see you and we have to discuss the
next task for you”, he communicated. Following your manager, you enter the meeting room. The
CEO and the senior developer already wait inside.
Your CEO said: “You did an excellent job! We are truly lucky, that you decided to join our
firm. Our clients were very pleased with the model that you designed for them. The report was
very helpful, they highlighted, that it was important for them to correctly understand the
limitations of the model and to know the expected performance after it is deployed in a real
world.
However, now we have another task for you. We want to start a trial with a major
hospital. I want you to prepare a proof-of-concept, so we can convince them, that a partnership
with our firm can be beneficial for them.
You will get a historical medical data. I want you to
design a model, that can classify, if a certain treatment is recommended for the patient or not.
Additionally, there are 6 additional features (denoted as TestA, TestB, GeneC, GeneD, GeneE
and GeneF in your dataset) that we can use. However, they are really expensive and difficult to
collect. I want you to assess, how useful they are. We meet with the hospital in two weeks. I
want a detailed report describing your main findings, on my desk, on Thursday, February 25, at
10
A.M.
Hint: Your boss called your task “proof-of-concept”, but in fact, the nature of that assignment is the same
as the last time. You are asked to train a classification model and you must measure how good that model
is.
Additionally, you must give recommendations which features are important to collect. You should look
at all variables, but at minimum, you should test the importance of TestA, TestB, GeneC, GeneD, GeneE
and GeneF.
Your Technical Manager said: “This time it really matters, that your model has a good
performance. If we can show that our model makes less mistakes than a human doctor, it would
be a big deal. Describe exactly how you tested your model. They are really going to look at that
section. Additionally, similar to the last time, the interpretability of the model is very important.
You should restrict yourself to logistic regression.”
Hint: Remember, that accuracy alone, is not a good measure. We care both, about accuracy, precision
and F1-score. Report also false positive and false negative. To choose a right model, you can use for
example the AUC score. It is ok (it’s even expected) that you will do some feature engineering. You can
also try to add regularization to your logistic regression* and test if it helps you or not. To show that the
model can be interpreted, you can identify and explain the most important relations between the variables
and the expected outcome (e.g., how the probability that the treatment is recommended changes with
age? Or gender?).
The Senior Developer took you aside and said: “My task is to deploy your model to
production. But I cannot deploy a paper-report. I need your code. However, remember that I am
not a Data Scientist list you. I have a different expertise. I will read your code, but you should
make sure that I can follow and understand it – and that I know how to use it.”
Hint: In the ideal case, people should be able to take your code, run it and recreate all your results. In a
less ideal case, it should be a demonstration of typical run. The code should demonstrate your approach
end-to-end. People should just specify the path to the dataset, run it and see final results. Another name
for this is a technical demo. At your future work, you might be quite often asked to demo your results.
People will expect you to present an end-to-end example where you read the raw data, train your model
and evaluate the results of the predictions.
Data
You can find the dataset ps2_public.csv on the Assignments section of Blackboard.
Report
To help you, I prepared a template. See https://www.overleaf.com/read/vnvhqxkpdhbk.
You are encouraged to use the template but you are free to use other editors or make
modifications. Just ensure that the final submission of the report has to be in PDF. Submission of
report has to be done on Blackboard.
Code Submission
We have created a GitHub Classroom where you can create private repositories. We will update
the class on Piazza on how to go about uploading your solutions on this platform.
Grading Rules
In order to grade your work, we will role-play the following situation. We will assume, that you
are a new-hire in our company. You are asked to provide a comprehensive technical report that
illustrates your findings. We will evaluate it from the perspective of three people.
• Your CEO (she would like to hear high level stuff. She will probably only read the
conclusions and look at main figure). (4 points for report).
• Your manager (he would like to see a detailed report; he might also look at some parts of
the code). (6 points for report and 2 points for code).
• A senior developer (they would like to see the code and won’t read the report at all). (8
points for code).
Your final score is: 10 points for report and 10 points for code.
Don’t Panic
Don’t panic. We understand that this is a large, open ended task. We also understand that this
might be the very first technical report that you were asked to write. We are dedicated to help
you do your best work all while keeping the standards high. We acknowledge that you have
limited time and resources to complete the task. This report doesn’t have to be perfect for 100%
score.
If you don’t know where to start read the Second Chapter of “Hands-On Machine Learning with
Scikit-Learn, Keras and Tensorflow”, 2nd Edition by Aurélien Géron. Check also the Appendix
B. Machine Learning Project Checklist from that book.
If something is not clear, ask your questions on Piazza.
Note: Cite any source you use (even if you adopt/copy a snippet of code). Failure to do so
would amount to plagiarism.
Optional Challenge
We also created an optional challenge for you. There is no additional credits for participating in
it. However, we encourage you to give it a try. We created a special class competition on Kaggle
(https://www.kaggle.com/c/usc-dsci552-section-32416d-spring-2021-ps2). Link to participate
in the competition: https://www.kaggle.com/t/420b31050f1248c4a2c95ca73a25051a. You will
find a special test dataset, where I removed the price column. Train a model (you are not
restricted to linear models anymore) and make your predictions. Have fun!
