Lab 9: K-Nearest Neighbors (KNN) Classification

Aim

Build a KNN classifier and classify a new sample using Euclidean distance.

Objectives

• Standardize intuition behind KNN.
• Fit KNeighborsClassifier with k=3.
• Predict class for [1,2].

Algorithm / Procedure

1. Define X, y for two classes.
2. Set k=3 and fit classifier.
3. Predict a new sample and discuss effect of k.

Python Code

from sklearn.neighbors import KNeighborsClassifier

X = [[0,0],[1,1],[2,2],[3,3]]
y = [0,1,1,0]

knn = KNeighborsClassifier(n_neighbors=3).fit(X, y)
print("Prediction for [1,2]:", knn.predict([[1,2]])[0])

Sample Output (expected)

Prediction for [1,2]: 1