Order ID |
53563633773 |
Type |
Essay |
Writer Level |
Masters |
Style |
APA |
Sources/References |
4 |
Perfect Number of Pages to Order |
5-10 Pages |
Description/Paper Instructions
UCI Machine Learning Repository Essay
Week 8 Individual Exercise
Deliverables: Two Files: (1) Submit this lab report with answers to all questions including output screenshots into the ‘Individual Exercises Week 8’ assignment folder. (2) Submit an R script that contains all commands with comments that briefly describe each commands purpose.
Part 2 – Run an exercise on the Vehicle Solhouettes dataset from vehicle.csv, completing this report and providing the commands, output screenshots, and discussion/interpretation as requested. Ensure that all commands are saved in this report AND in an R script.
For Reference: UCI Machine Learning Repository: Vehicle Silhouettes
- Introduction:
- Based on what you have learned this week about k-means clustering, provide a one-paragraph masters-level response describing what you anticipate that the kmeans method will accomplish for the Vehicle Silhouettes data? Be specific about the behavior and output structure of k-means models. (80-120 words)
- Data Pre-Processing: Load the Vehicle Silhouettes data into R Studio using the read.csv command (do not use File > Import Dataset > From CSV in the R Studio GUI as this uses read_csv() resulting in significant different variable types!!!).
- Make a copy of the loaded Vehicle Silhouettes data you just imported and name the copy ‘myvehicle’. Keep the original import as you will need both the original and copy to complete this report. Include the command demonstrating this step below.
Command: >
- Remove the variable class from ‘myvehicle’. Include the command and answer to the question below.
Command: >
Why do we need to remove the class variable as part of the data preprocessing steps for k-means clustering?
iii. Run the scale() function on ‘myvehicle’. Include the command and answer to the question below. (Note: This command is NOT part of your tutorial. Consult the function help and use the default arguments. Hint: scale() is a function that outputs its results. You MUST save the scaled output back to the original ‘myvehicle’.
Command: >
Why must we scale data as part of the data preprocessing steps for k-means clustering?
- What additional data preprocessing steps (if any) did you need to execute? Include the command(s) and output screenshot below.
Command(s): >
Output:
- K-Means Clustering – Running the Method (Hint: Record your results with k=4 in the table in part f):
- Run ‘set.seed(12345)’ and then run the kmeans method with k=4 and store the output to a variable named ‘kc’. Include the command, output screenshot, and discuss the input parameters you used.
Command: >
Output:
Discussion:
- Enter ‘kc’ at the prompt. Provide the output below and then answer the following questions:
Output:
How many instances are in each cluster?
What information does the cluster means section provide and how were those numbers obtained?
What is the clustering vector?
What is the sum of squares by clusters and what does it mean?
iii. Run the ‘kc$iter’ command. Include the command, output screenshot, and explain what the output shows.
Command: >
Output:
Discussion:
- K-Means Clustering – Evaluate the Model:
- Build the cross-tabulation to compare how the method clustered the vehicles from ‘myvehicle’ to the actual vehicle class from your original import. Include the command, output screenshot, and answer the following questions:
Command: >
Output:
What is the dominant vehicle class in each cluster?
What is the dominant cluster for each vehicle class?
What percentage of vehicles were clustered in agreement with the actual class?
- K-Means Clustering – Cluster Visualization:
- Run the ‘clusplot(kc)’ function to visualize your model. Modify the plot appearance to make your visualization clear and easy to interpret. Unlike previous exercises, your visualization will now be evaluated on clarity and aesthetics in addition to the standard command, output, and interpretation evaluation. Include the full command, output screenshot (zoomed in), and a one-paragraph, masters-level response with your interpretation of your plot.
(Hint: Your interpretation should discuss all of the visualized clusters and should begin to address specific observations (data points) within each that warrant discussion.)
Command: >
Output:
Interpretation: (80-120 words)
- K-Means Clustering – Experiment with Different K Values (3 Runs Summarized):
- Completely fill in the table below documenting the results of your experimentation with modifying the k value. You may use any k value other than 4 that is greater than 0. You do not need to provide any commands or output screenshots in this report. However, you will be evaluated on these commands being present in your R script!
k= |
Number of Instances in Each Cluster |
Between Clusters Sum of Squares |
Within Clusters Sum of Squares |
Number of Iterations |
4 |
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- What effect do you observe that modifying the k values has on the method results? Provide a one-paragraph, masters-level response below:
iii. What is an ideal value of k for the Vehicle Silhouettes data? This is a subjective and open-ended question. Challenge yourself and come up with a creative and well-supported answer for which value you believe is ideal. Provide a one-paragraph, masters-level response below: (80-120 words)
- Summary:
- What differences between k-means clustering and classification methods did you observe? Provide a one-paragraph, masters-level response. (80-120 words)
- Which part of this exercise did you find the most challenging and what steps did you take to resolve the challenge?
References
RUBRIC
QUALITY OF RESPONSE |
NO RESPONSE |
POOR / UNSATISFACTORY |
SATISFACTORY |
GOOD |
EXCELLENT |
Content (worth a maximum of 50% of the total points) |
Zero points: Student failed to submit the final paper. |
20 points out of 50: The essay illustrates poor understanding of the relevant material by failing to address or incorrectly addressing the relevant content; failing to identify or inaccurately explaining/defining key concepts/ideas; ignoring or incorrectly explaining key points/claims and the reasoning behind them; and/or incorrectly or inappropriately using terminology; and elements of the response are lacking. |
30 points out of 50: The essay illustrates a rudimentary understanding of the relevant material by mentioning but not full explaining the relevant content; identifying some of the key concepts/ideas though failing to fully or accurately explain many of them; using terminology, though sometimes inaccurately or inappropriately; and/or incorporating some key claims/points but failing to explain the reasoning behind them or doing so inaccurately. Elements of the required response may also be lacking. |
40 points out of 50: The essay illustrates solid understanding of the relevant material by correctly addressing most of the relevant content; identifying and explaining most of the key concepts/ideas; using correct terminology; explaining the reasoning behind most of the key points/claims; and/or where necessary or useful, substantiating some points with accurate examples. The answer is complete. |
50 points: The essay illustrates exemplary understanding of the relevant material by thoroughly and correctly addressing the relevant content; identifying and explaining all of the key concepts/ideas; using correct terminology explaining the reasoning behind key points/claims and substantiating, as necessary/useful, points with several accurate and illuminating examples. No aspects of the required answer are missing. |
Use of Sources (worth a maximum of 20% of the total points). |
Zero points: Student failed to include citations and/or references. Or the student failed to submit a final paper. |
5 out 20 points: Sources are seldom cited to support statements and/or format of citations are not recognizable as APA 6th Edition format. There are major errors in the formation of the references and citations. And/or there is a major reliance on highly questionable. The Student fails to provide an adequate synthesis of research collected for the paper. |
10 out 20 points: References to scholarly sources are occasionally given; many statements seem unsubstantiated. Frequent errors in APA 6th Edition format, leaving the reader confused about the source of the information. There are significant errors of the formation in the references and citations. And/or there is a significant use of highly questionable sources. |
15 out 20 points: Credible Scholarly sources are used effectively support claims and are, for the most part, clear and fairly represented. APA 6th Edition is used with only a few minor errors. There are minor errors in reference and/or citations. And/or there is some use of questionable sources. |
20 points: Credible scholarly sources are used to give compelling evidence to support claims and are clearly and fairly represented. APA 6th Edition format is used accurately and consistently. The student uses above the maximum required references in the development of the assignment. |
Grammar (worth maximum of 20% of total points) |
Zero points: Student failed to submit the final paper. |
5 points out of 20: The paper does not communicate ideas/points clearly due to inappropriate use of terminology and vague language; thoughts and sentences are disjointed or incomprehensible; organization lacking; and/or numerous grammatical, spelling/punctuation errors |
10 points out 20: The paper is often unclear and difficult to follow due to some inappropriate terminology and/or vague language; ideas may be fragmented, wandering and/or repetitive; poor organization; and/or some grammatical, spelling, punctuation errors |
15 points out of 20: The paper is mostly clear as a result of appropriate use of terminology and minimal vagueness; no tangents and no repetition; fairly good organization; almost perfect grammar, spelling, punctuation, and word usage. |
20 points: The paper is clear, concise, and a pleasure to read as a result of appropriate and precise use of terminology; total coherence of thoughts and presentation and logical organization; and the essay is error free. |
Structure of the Paper (worth 10% of total points) |
Zero points: Student failed to submit the final paper. |
3 points out of 10: Student needs to develop better formatting skills. The paper omits significant structural elements required for and APA 6th edition paper. Formatting of the paper has major flaws. The paper does not conform to APA 6th edition requirements whatsoever. |
5 points out of 10: Appearance of final paper demonstrates the student’s limited ability to format the paper. There are significant errors in formatting and/or the total omission of major components of an APA 6th edition paper. They can include the omission of the cover page, abstract, and page numbers. Additionally the page has major formatting issues with spacing or paragraph formation. Font size might not conform to size requirements. The student also significantly writes too large or too short of and paper |
7 points out of 10: Research paper presents an above-average use of formatting skills. The paper has slight errors within the paper. This can include small errors or omissions with the cover page, abstract, page number, and headers. There could be also slight formatting issues with the document spacing or the font Additionally the paper might slightly exceed or undershoot the specific number of required written pages for the assignment. |
10 points: Student provides a high-caliber, formatted paper. This includes an APA 6th edition cover page, abstract, page number, headers and is double spaced in 12’ Times Roman Font. Additionally, the paper conforms to the specific number of required written pages and neither goes over or under the specified length of the paper. |
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