K-Nearest Neighbors

easy· Machine Learningruns: 0

Implement the K-Nearest Neighbors classifier from scratch. Given training features and labels, predict the label of each query point by majority vote among its k closest neighbors using Euclidean distance.

wpm 0acc 100%time 0:000 / 510

import numpy as np
from collections import Counter

class KNN:
    def __init__(self, k: int = 3):
        self.k = k

    def fit(self, X, y):
        self.X_train = X
        self.y_train = y

    def predict(self, X):
        return np.array([self._predict_one(x) for x in X])

    def _predict_one(self, x):
        distances = np.linalg.norm(self.X_train - x, axis=1)
        k_idx = np.argsort(distances)[: self.k]
        labels = self.y_train[k_idx]
        return Counter(labels).most_common(1)[0][0]

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