2nd/10_Wiki/Topics/AI_and_ML/K-Nearest-Neighbors-K-NN.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-k-nearest-neighbors-k-nn	K-Nearest Neighbors (k-NN)	10_Wiki/Topics	verified	self	k-NN kNN nearest neighbor lazy learning FAISS instance-based	none	A	0.96	applied	machine-learning knn classification regression faiss retrieval		2026-05-10	pending	language framework Python scikit-learn / FAISS / Annoy

K-Nearest Neighbors (k-NN)

매 한 줄

"매 query 의 의 의 K closest training point 의 의 의 의 vote/avg". 매 lazy learning (no training). 매 simple but effective baseline. 매 modern: 매 vector DB의 backbone (FAISS, Pinecone). 매 RAG retrieval 도 결국 k-NN.

매 핵심

매 task

• Classification: 매 majority vote.
• Regression: 매 average.
• Density estimation.
• Anomaly detection.

매 distance

• Euclidean (L2).
• Cosine (text/embed).
• Manhattan (L1).
• Hamming (binary).
• Custom (Mahalanobis).

매 efficiency

• Brute force: O(N).
• KD-tree (low-dim).
• Ball tree.
• HNSW (FAISS, modern).
• IVF (inverted file).
• PQ (product quantization).

매 응용

1. Image retrieval.
2. Recommendation.
3. RAG retrieval.
4. Anomaly detection.
5. Baseline classifier.

💻 패턴

Basic (sklearn)

from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5, weights='distance', metric='euclidean')
knn.fit(X_train, y_train)
preds = knn.predict(X_test)

Cosine (for embeddings)

knn = KNeighborsClassifier(n_neighbors=5, metric='cosine')

KD-tree (for low-dim)

from sklearn.neighbors import KDTree
tree = KDTree(X)
distances, indices = tree.query(X_query, k=5)

FAISS (large-scale)

import faiss
import numpy as np

d = 768
index = faiss.IndexFlatIP(d)  # 매 inner product
faiss.normalize_L2(X)
index.add(X)

faiss.normalize_L2(query)
D, I = index.search(query, k=10)

FAISS HNSW (approximate, fast)

index = faiss.IndexHNSWFlat(d, M=32)
index.hnsw.efConstruction = 200
index.add(X)
index.hnsw.efSearch = 50
D, I = index.search(query, k=10)

FAISS IVF + PQ (massive scale)

nlist = 100
quantizer = faiss.IndexFlatL2(d)
index = faiss.IndexIVFPQ(quantizer, d, nlist, 8, 8)  # 매 8 sub-quantizers, 8 bits each
index.train(X)
index.add(X)
index.nprobe = 10  # 매 search trade-off
D, I = index.search(query, k=10)

Annoy (alternative)

from annoy import AnnoyIndex
index = AnnoyIndex(d, 'angular')  # 매 cosine
for i, v in enumerate(vectors):
    index.add_item(i, v)
index.build(n_trees=10)
neighbors = index.get_nns_by_vector(query, 10)

Custom distance

from sklearn.neighbors import KNeighborsClassifier

def custom_dist(a, b):
    return np.sum(np.abs(a - b))  # 매 Manhattan

knn = KNeighborsClassifier(n_neighbors=5, metric=custom_dist)

Weighted by distance

knn = KNeighborsClassifier(n_neighbors=5, weights='distance')
# 매 매 closer = 매 higher weight in vote

k-NN regression

from sklearn.neighbors import KNeighborsRegressor
knr = KNeighborsRegressor(n_neighbors=5).fit(X, y)

Anomaly detection (LOF)

from sklearn.neighbors import LocalOutlierFactor
lof = LocalOutlierFactor(n_neighbors=20)
anomalies = lof.fit_predict(X) == -1

k-NN with normalization (always!)

from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
pipe = Pipeline([('scaler', StandardScaler()), ('knn', KNeighborsClassifier(5))])
pipe.fit(X, y)

Choose K (CV)

from sklearn.model_selection import GridSearchCV
grid = GridSearchCV(KNeighborsClassifier(), {'n_neighbors': [3, 5, 7, 11, 15]}, cv=5)
grid.fit(X, y)
print(grid.best_params_)

RAG retrieval (k-NN over embeddings)

from sentence_transformers import SentenceTransformer
m = SentenceTransformer('all-mpnet-base-v2')
doc_embs = m.encode(documents)

import faiss
index = faiss.IndexFlatIP(doc_embs.shape[1])
faiss.normalize_L2(doc_embs)
index.add(doc_embs)

def retrieve(query, k=5):
    q_emb = m.encode([query])
    faiss.normalize_L2(q_emb)
    _, I = index.search(q_emb, k)
    return [documents[i] for i in I[0]]

kNN-LM (LLM augmentation)

def knn_lm_predict(context, llm, datastore, k=10):
    """매 LLM logit + retrieve nearest neighbor logit (Khandelwal 2020)."""
    llm_logits = llm.next_token_logits(context)
    nn_logits = datastore.knn_logits(context_emb=context.encode(), k=k)
    return llm_logits + 0.25 * nn_logits  # 매 simple interpolation

매 결정 기준

상황	Approach
Small data	sklearn brute / KD-tree
High-dim	FAISS HNSW
Massive scale	FAISS IVF+PQ
Production search	Pinecone / Weaviate
Anomaly	LOF
RAG	FAISS / vector DB

기본값: 매 normalize 의 always + 매 cosine for embed + 매 FAISS HNSW for prod + 매 CV-tuned K + 매 weighted-by-distance.

🔗 Graph

• 부모: Machine-Learning · Information Retrieval
• 변형: HNSW
• 응용: FAISS · RAG
• Adjacent: Embeddings

🤖 LLM 활용

언제: 매 baseline. 매 retrieval. 매 RAG. 언제 X: 매 high-dim raw (use embed first).

❌ 안티패턴

• No normalize: 매 magnitude dominate.
• Brute force at scale: 매 latency.
• Wrong K: 매 underfit/overfit.
• No metric thought: 매 cosine vs L2 의 wrong.

🧪 검증 / 중복

• Verified (Cover & Hart 1967, FAISS docs, Khandelwal kNN-LM 2020).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — k-NN + 매 sklearn / FAISS / HNSW / IVF / RAG / kNN-LM code