Modified: 2014/09/28 12:28 by admin - Uncategorized
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The Task: Implement Random Forest, Adaboost and Gradient Boosting (Due on Nov. 17, 2014)

  • Implement the Random Forest Algorithm (Information on Random Forest can be found here).
  • Implement the Adaboost Algorithm (Information on Adaboost can be found here).
  • Implement the Gradient Boosting Algorithm (Information on Gradient Boosting can be found here).
  • The Decision Tree implemented in the previous assignment can be used as base classifier of Random Forest, Adaboost and Gradient Boosting.
  • Conduct 10 fold cross validation on the benchmark data used in Assignment 1 by Random Forest, Adaboost and Gradient Boosting respectively, report the mean and standard deviation of accuracy.
  • Write a brief report to show your results.


Benchmark Dataset

The same as Assignment 1.


Programming Language

  • The choice you have made in the first assignment



  • Please use this MSWord template to report your results.
  • Do NOT plagiarize, plagiarism will be seriously penalized: You should be careful on writing your report. Whenever you are using words and works of others, citations should be made clear such that one can tell which part is actually yours. Details about how to identify a plagiarism can be found in "Introduction to the Guidelines for Handling Plagiarism Complaints".
  • Do NOT falsify results, data fraud will be even more seriously penalized: You should honestly record your results in the report, NEVER EVER modify the performance results manually.
  • Pack your report and code into a zip file named with your student ID, e.g., ''. If you have multiple submissions, add an extra '_' with a number, such as ''. We will use the the version with the largest number as your final submission.

    • The file format should be zip, no other format is acceptable!
    • NO submission after the deadline is acceptable!
    • NO email submission will be accepted!

Upload your file to FTP: (please use FTP software to upload, do not use Windows Explorer or IE)
username/password: You will be informed in the first class



We will evaluate your submission according to your implementation and report.

For implementation :
  • Efficiency
  • Performance
  • Code style

For report:
  • Technique: clearly explain all the component you used in your implementation
  • Language: concise, precise, and logical.

If plagiarism is identified, no scores will be given to this report.


Contact TA

Mr. Qing Da and Mr. Yue Zhu

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