University : Others UniLearnO is not sponsored or endorsed by this college or university.
Country : Australia

MATLAB Assessment 

ENN543

Assignment Task

Problem 1.

Linear Regression. Prediction of residuary resistance of sailing yachts at the initial design stage is of a great value for evaluating the ship’s performance and for estimating the required propulsive power. Essential inputs include the basic hull dimensions and the boat velocity. The Delft data set comprises 308 full-scale experiments, which were performed at the Delft Ship Hydromechanics Laboratory for that purpose. The results of these experiments are in the file yacht.dat. These experiments include 22 different hull forms, derived from a parent form closely related to the “Standfast” designed by Frans Maas. The columns correspond to the following variables (in order):
• Residuary resistance per unit weight of displacement, adimensional;
• Longitudinal position of the center of buoyancy, adimensional;
• Prismatic coefficient, adimensional;
• Length-displacement ratio, adimensional;
• Beam-draught ratio, adimensional;
• Length-beam ratio, adimensional;
• Froude number, adimensional.
Using this data:
1. Using fitlm in MATLAB, fit a model to predict the resistance per unit weight of displacement as a function of the other variables. Discuss if this is a valid model.
2. Given the above model as a starting point, investigate how it can be improved. In this you should consider:
(a) The use of training and validation datasets. The data should be divided such that the split between these two sets is approximately 80% for training and 20% for validation.
(b) Are all variables important for the model?

Problem 2. Regularised Regression (20%). Web pages collect large volumes of data on page views, page links, etc., to monitor readership. For commercial ventures, this can help inform publishing and layout decisions, as well as advertising. The BlogFeedback dataset contains data on blog readership, and can be used to predict page views in the next 24 hours based on past readership data.
You have been supplied with two variants of this data:
1. Files named blogData noBow train.csv and blogData noBow test.csv contain features that capture the average readership information for the blog, and information for the specific post (see blogData Variables.txt for further information);
2. Files named blogData train.csv and blogData test.csv contains all the features of the noBow files alongside 200 bag-of-words features1 that capture the blog post content.
Note that the testing data contains examples from later times to the training data, simulating a real-world case where the model is trained on historic data to predict the future.
Using this data:
1. Fit a model using Linear regression, Ridge and LASSO regression on noBowdata. With these models consider the following:
(a) Determine the best value of λ to use in the Ridge model to obtain the best predictive model.
(b) Determine the best value of λ to use in the LASSO model to obtain the best predictive model.

2. Fit a model using Linear regression, Ridge and LASSO regression on the data containing the Bag-of-Words features. With these models consider the following:

(a) Determine the best value of λ to use in the Ridge model to obtain the best predictive model.
(b) Determine the best value of λ to use in the LASSO model to obtain the best predictive model.
3. Compare the performance of the two Linear, Ridge and LASSO models. You should consider factors such as the errors of the models, the R2 and Adjusted R2, and the
model validity in your discussion. Which, if any, models are suitable for use? Justify your response.

This MATLAB Assessment has been solved by our MATLAB experts at UniLearnO. Our Assignment Writing Experts are efficient to provide a fresh solution to this question. We are serving more than 10000+ Students in Australia, UK & US by helping them to score HD in their academics. Our Experts are well trained to follow all marking rubrics & referencing style.

Be it a used or new solution, the quality of the work submitted by our assignment experts remains unhampered. You may continue to expect the same or even better quality with the used and new assignment solution files respectively. There’s one thing to be noticed that you could choose one between the two and acquire an HD either way. You could choose a new assignment solution file to get yourself an exclusive, plagiarism (with free Turnitin file), expert quality assignment or order an old solution file that was considered worthy of the highest distinction.

  • Uploaded By : Keith
  • Posted on : September 09th, 2018
  • Downloads : 154

Whatsapp Tap to ChatGet instant assistance