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Regularization

Overview and Rationale

In order to consolidate your theoretical knowledge into technique and skills with practical

and applicational value, you will use the glmnet() package in R to implement LASSO

function to build linear and logistic models through LASSO over values of regularization

parameter lambda.

Course Outcomes

This assignment is directly linked to the following key learning outcomes from the course

syllabus:

• Conduct regularization method for models to describe relationships among

variables and make useful predictions

Assignment Summary

Use one of the real world example data sets from R (not previously used in the R practice

assignment) or a dataset you have found, to build regularization models by using Lasso

(least absolute shrinkage and selection operator) and extend Lasso model fitting to big data

that cannot be loaded into memory. You will fit solution paths for linear or logistic

regression models penalized by Lasso over a grid of values for the regularization

parameter lambda.

Use the resources in this module to guide your R code development.

Report

Your assignment/project should have a good cover/title page, introduction of what the

goals of the project and the methods you use. It also should follow APA format with at least

1000 words (excluding title page and references page) and references page. In the body of

your project you should incorporate the R codes and R outputs with interpretation of your

results. Be sure to show all the elements in the official hypothesis, including the null and

alternative hypothesis, the critical values, calculation of the test statistics and p-values.

Finally, you need to make sense of your results to make good points with proper

conclusions, to show your understanding of the course material and its application to the

dataset.

Graphs, figures, charts, tables are very useful to increase visual effects to impress your

readers. You also should do your best to give insight and understanding to the project with

a good conclusion. Please use subtitles to make your assignment more reader friendly as

well.

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