Project 3: Classification Algorithms




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Project 3: Classification Algorithms

Hard Copy Report: bring to your demo.
Please clearly state the UB Person numbers and UB IT names for all the
group members on the cover of the report.
Two datasets (project3_dataset1and project3_dataset2) can be found on Piazza. Please check the
README file first for a short description of the two datasets.
Complete the following tasks:
 Implement three classification algorithms by yourself: Nearest Neighbor, Decision
Tree, and Naïve Bayes.
 Implement Random Forests based on your own implementation of Decision Tree.
 Implement Boosting based on your own implementation of Decision Tree.
 Adopt 10-fold Cross Validation to evaluate the performance of all methods on the
provided two datasets in terms of Accuracy, Precision, Recall, and F-1 measure.
Your final submission should include the following:
 Code: Implementation of five methods. All the methods must be implemented by
yourself. Existing packages or online codes for the algorithms are not allowed. Together
with your code submission, a README file should be included to explain how to execute
your code.
 Report: Describe the flow of all the implemented methods, and describe the choice you
make (such as parameter setting, pre-processing, post-processing, how to deal with overfitting, etc.). Compare their performance, and state their pros and cons based on your
The details about Demo will be released on Nov. 30 through Piazza. Please note:
 New datasets will be given to check your implemented classification methods and
performance measures. The data format will be consistent with the README file that we
already provided.
 During the demo, you will be asked to adopt specific setting and run your code.
Note that copying code/results/report from another group or source is not allowed and may
result in an F in the grades of all the team members. Academic integrity policy can be found at