Do part C
Problem The Fisher's Iris data set is a dataset containing 150 samples, each of which has four
attributes Petal Length, Petal Width, Sepal Length, Sepal width and a species label. The species
can be predicted based on the four features associated with each sample. More details about the
dataset are available in this Wikipedia page: https:/len. wikipedia.org/wiki/lris flower data set. The
data can be assessed by loading the mat file provided in this assignment. Two items
Irismeasurements and Irisclass should be loaded into the workspace. Irismeasurements is a 1504
matrix containing the four features of 150 iris samples, with each row corresponding to the
features of each sample. Irisclass is a 150-element column vector specifying the species of the
150 samples. In Irisclass, 1, 2, 3 encode the species setosa, versicolor and virginica, respectively.
(a) (60 marks) Write a Python function called KNearestNeighbour that uses the KNN algorithm to
classify a test set based on a training set (i.e., determine the species of each data point in the test
set). Save the function in a file named KNearestNeighbour.py. Your function should be well-
commented. The function header should have the form: def KNearestNeighbour
(TestMeasurements, TrainMeasurements, Trainclass, K) where Testmeasurements is an M4
matrix containing the feature vector of M test samples, TrainMeasurements is a N4 matrix
containing the feature vectors of the training set and TrainClass is a N-element vector containing
the species label of the N training samples, K is the parameter K used in KNN. The function
should return a M element vector showing classification results generated by KNN for the test
samples. You are allowed to use all built-in functions except those related to kNeighborsClassifier,
which is the built-in class for KNN classification. (b) (10 marks) Write a Python script to test the
function you wrote in Part (a). Use the first 100 samples in the data set as training set and the last
50 samples as the testing set. Set K= 5. Your test code should be written and saved in a Jupyter
notebook called TestKNearestNeighbour. ipynb. (c) (30 marks) Write a Python script to implement
the lockbox approach. Save the script in a Jupyter notebook called LockBoxKNN . ipynb. Use the
first 50 samples as the training data, the next 50 samples as validation data and the last 50
samples as the test data. Use the function you wrote in Part (a) for all KNN classifications
required. When tuning the parameter K, consider all even K inside the range 1K21. Tabulate the
accuracy for.
Do part C Problem The Fishers Iris data set is a dataset co.pdf
1. Do part C
Problem The Fisher's Iris data set is a dataset containing 150 samples, each of which has four
attributes Petal Length, Petal Width, Sepal Length, Sepal width and a species label. The species
can be predicted based on the four features associated with each sample. More details about the
dataset are available in this Wikipedia page: https:/len. wikipedia.org/wiki/lris flower data set. The
data can be assessed by loading the mat file provided in this assignment. Two items
Irismeasurements and Irisclass should be loaded into the workspace. Irismeasurements is a 1504
matrix containing the four features of 150 iris samples, with each row corresponding to the
features of each sample. Irisclass is a 150-element column vector specifying the species of the
150 samples. In Irisclass, 1, 2, 3 encode the species setosa, versicolor and virginica, respectively.
(a) (60 marks) Write a Python function called KNearestNeighbour that uses the KNN algorithm to
classify a test set based on a training set (i.e., determine the species of each data point in the test
set). Save the function in a file named KNearestNeighbour.py. Your function should be well-
commented. The function header should have the form: def KNearestNeighbour
(TestMeasurements, TrainMeasurements, Trainclass, K) where Testmeasurements is an M4
matrix containing the feature vector of M test samples, TrainMeasurements is a N4 matrix
containing the feature vectors of the training set and TrainClass is a N-element vector containing
the species label of the N training samples, K is the parameter K used in KNN. The function
should return a M element vector showing classification results generated by KNN for the test
samples. You are allowed to use all built-in functions except those related to kNeighborsClassifier,
which is the built-in class for KNN classification. (b) (10 marks) Write a Python script to test the
function you wrote in Part (a). Use the first 100 samples in the data set as training set and the last
50 samples as the testing set. Set K= 5. Your test code should be written and saved in a Jupyter
notebook called TestKNearestNeighbour. ipynb. (c) (30 marks) Write a Python script to implement
the lockbox approach. Save the script in a Jupyter notebook called LockBoxKNN . ipynb. Use the
first 50 samples as the training data, the next 50 samples as validation data and the last 50
samples as the test data. Use the function you wrote in Part (a) for all KNN classifications
required. When tuning the parameter K, consider all even K inside the range 1K21. Tabulate the
accuracy for
