Description
• Part 1: Build a classifier based on KNN (K=5 for testing) using Euclidean distance.
o You are expected to code the KNN classifier by yourself.
o Report performance using an appropriate k-fold cross validation using confusion
matrices on both datasets.
o Report the run time performance of your above tests.
• Part 2: Build a classifier based on KNN (K=5 for testing) using Manhattan distance.
o You are expected to code the KNN classifier by yourself.
o Report performance using an appropriate k-fold cross validation using confusion
matrices on both datasets.
o Report the run time performance of your above tests.
• Part 3: Build a classifier based on linear SVM.
o You may use an available implementation of SVM in Python.
o Report performance using an appropriate k-fold cross validation using ROC curves and
confusion matrices. Find the best threshold for the SVM output as described in the
note by Fawcett.
o Report the run time performance of your above tests.
• Part 4: Build a classifier based on polynomial SVM.
o You may use an available implementation of SVM in Python.
o Report performance using an appropriate k-fold cross validation using ROC curves and
confusion matrices. Find the best threshold for the SVM output as described in the
note by Fawcett.
o Report the run time performance of your above tests.
• Part 5 (optional): Improve your search procedure in Part 1 and Part 2 using an advanced search
algorithm such as kd-trees.
What to hand in: You are expected to hand in one of the following
• HW1_lastname_firstname_studentnumber_code.ipynb (the Python notebook file containing
the code and report output).
Your notebook should include something like the following:
Part 1:
Code:
Results:
Comments:
