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update on project 1

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Morten Hjorth-Jensen
2024-09-03 04:59:06 +02:00
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doc/src/Projects/2024/Project1/Project1.do.txt
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@@ -3,28 +3,34 @@ AUTHOR: "Data Analysis and Machine Learning FYS-STK3155/FYS4155":"http://www.uio
DATE: September 2
===== Preamble: Note on writing reports, using reference material, AI and other tools =====
We want you to answer the three different projects by handing reports written like a standard scientific/technical report.
The link at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/ProjectWriting/projectwriting.ipynb" gives some guidance. See also the grading suggestion at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md".
We want you to answer the three different projects by handing in
reports written like a standard scientific/technical report. The link
at
URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/ProjectWriting/projectwriting.ipynb"
gives some guidance. See also the grading suggestion at
URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md".
When using codes from different sources that you have not developed yourself,
you should refer to these in the bibliography of your report, indicating wherefrom you
Furthermore, at
URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/ReportExample/"
you can find examples of previous reports. How to write reports will
also be discussed during the various lab sessions. Please do ask us if you are in doubt.
When using codes and material from other sources, you should refer to these in the bibliography of your report, indicating wherefrom you for example
got the code, whether this is from the lecture notes, softwares like
Scikit-Learn, TensorFlow, PyTorch or other sources. These should
always be cited correctly. How to cite some of the libraries is often
indicated from their corresponding GitHub sites or websites, see for example how to cite Scikit-Learn at URL:"https://scikit-learn.org/dev/about.html".
We enocurage you to use tools like
"ChatGPT":"https://openai.com/chatgpt/" in writing the report. If you use for example ChatGPT,
"ChatGPT":"https://openai.com/chatgpt/" or similar in writing the report. If you use for example ChatGPT,
please do cite it properly and include (if possible) your questions and answers as an addition to the report. This can
be uplodaed to for example your website, GitHub/GitLab or similar as supplemental material.
be uploaded to for example your website, GitHub/GitLab or similar as supplemental material.
On scaling, we recommend reading the following section from the scikit-learn software description, see URL:"https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#plot-all-scaling-standard-scaler-section"
@@ -36,14 +42,8 @@ On scaling, we recommend reading the following section from the scikit-learn sof
The main aim of this project is to study in more detail various
regression methods, including the Ordinary Least Squares (OLS) method.
In addition to the scientific part, in this course we want also to
give you an experience in writing scientific reports. The format for
the delivery of your answers is namely that of a scientific report. At
for example
URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md"
we detail how to write a report. Furthermore, at
URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/ReportExample/"
you can find examples of previous reports. How to write reports will
also be discussed during the various lab sessions.
give you an experience in writing scientific reports.
_A small recommendation when developing the codes here_. Instead of
@@ -151,6 +151,8 @@ plt.show()
!ec
If you wish to compare your results with other on the Franke function or other popular functions tested with linear regression, see the list in Figure 1 of the article by Cook et al at URL:"https://arxiv.org/abs/2401.11694".
=== Part a) : Ordinary Least Square (OLS) on the Franke function ===
We will generate our own dataset for a function
@@ -213,6 +215,9 @@ approximately $2/3$ to $4/5$ of the data as training data.
You can easily reuse the solutions to your exercises from week 35 and week 36.
See also the lecture slides from week 35 and week 36.
On scaling, we recommend reading the following section from the scikit-learn software description, see URL:"https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#plot-all-scaling-standard-scaler-section".
=== Part b): Adding Ridge regression for the Franke function ===
@@ -381,18 +386,16 @@ You can follow the code example in the jupyter-book at URL:"https://compphysics.
=== Part f): Cross-validation as resampling techniques, adding more complexity ===
The aim here is to write your own code for another widely popular
The aim here is to implement another widely popular
resampling technique, the so-called cross-validation method.
Implement the $k$-fold cross-validation algorithm (write your own
code) and evaluate again the MSE function resulting
from the test folds. You can compare your own code with that from
_Scikit-Learn_ if needed.
code or use the functionality of _Scikit-Learn_) and evaluate again the MSE function resulting
from the test folds.
Compare the MSE you get from your cross-validation code with the one
you got from your _bootstrap_ code. Comment your results. Try $5-10$
folds. You can also compare your own cross-validation code with the
one provided by _Scikit-Learn_.
folds.
In addition to using the ordinary least squares method, you should include both Ridge and Lasso regression.