Part I Single Feature

$1.$ Create a matlab script that will perform each of the steps required for this exercise.

$2.$ Load $$partOneData$.$mat$$ into the matlab environment $($previously used for assignment one$).$

$3.$ Generate a random partition of the data, splitting each of the classes into $60\%$ training and $40\%$

testing.

a$.$ Using only the training data, classify each of the test samples using the K$-$Nearest

Neighbor Classifier.

b$.$ Use the Euclidean Distance as the distance metric.

c$.$ Print out the total prediction accuracy using the fprintf commands.

Part II Multivariate

$1.$ Create a matlab script that will perform each of the steps required for this exercise.

$2.$ Load $$partTwoData$.$mat$$ into the matlab environment $($previously used for assignment one$).$

$3.$ Create a random partition of the data, splitting each of the classes into $60\%$ training and $40\%$

testing.

a$.$ Using only the training data, classify each of the test samples using the K$-$Nearest

Neighbor Classifier with k $=1.$

b$.$ Use the Euclidean Distance as the distance metric.

c$.$ Print out the total prediction accuracy using the fprintf commands.Part I Single Feature

Create a matlab script that will perform each of the steps required for this exercise.

Load 'partOneData.mat' into the matlab environment $($previously used for assignment one$).$

Generate a random partition of the data, splitting each of the classes into $60\%$ training and $40\%$

testing.

a$.$ Using only the training data, classify each of the test samples using the K$-$Nearest

Neighbor Classifier.

b$.$ Use the Euclidean Distance as the distance metric.

c$.$ Print out the total prediction accuracy using the fprintf commands.

Part II Multivariate

Create a matlab script that will perform each of the steps required for this exercise.

Load 'partTwoData.mat' into the matlab environment $($previously used for assignment one$).$

Create a random partition of the data, splitting each of the classes into $60\%$ training and $40\%$

testing.

a$.$ Using only the training data, classify each of the test samples using the K$-$Nearest

Neighbor Classifier with k$=1.$

b$.$ Use the Euclidean Distance as the distance metric.

c$.$ Print out the total prediction accuracy using the fprintf commands.

Do NOT use the Statistics and Machine Learning Toolbox.