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K-Nearest Neighbors (KNN) Classifiermedium

K-Nearest Neighbors (KNN) Classifier

mediumMachine Learning⏱ 15 min
⚡ Libraries allowed · Pure Python earns +10 bonus XP
🎯 Mission
Implement K-Nearest Neighbors classifier by computing Euclidean distances to training samples.

Task

Implement `knn_predict(X_train, y_train, x_test, k)` returning predicted integer class label.

Function Signature

knn_predict(X_train: list[list[float]], y_train: list[int], x_test: list[float], k: int) -> int

Examples

Example 1: 2D Classification k=3
Input: {"X_train":[[0,0],[0,1],[1,0],[5,5],[5,6]],"y_train":[0,0,0,1,1],"x_test":[0.5,0.5],"k":3}
Output: 0

Constraints

  • Compute Euclidean distance for each training sample. Select top k nearest labels.
Python3Saved ✓
import math
from collections import Counter

def knn_predict(X_train, y_train, x_test, k):
"""
Predicts majority class for x_test based on k nearest neighbors by Euclidean distance.
"""
# Your implementation here
pass

Case 1: 2D Classification k=3
Input: {"X_train":[[0,0],[0,1],[1,0],[5,5],[5,6]],"y_train":[0,0,0,1,1],"x_test":[0.5,0.5],"k":3}
Expected: 0