2nd/10_Wiki/Topics/Domain_Programming/AI_and_ML/Term-Frequency-Inverse-Document-Frequency.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-term-frequency-inverse-document-	Term Frequency-Inverse Document Frequency	10_Wiki/Topics	verified	self	TF-IDF tfidf classic IR baseline	none	A	0.95	applied	ir nlp retrieval baseline sklearn		2026-05-10	pending	language framework python scikit-learn

Term Frequency-Inverse Document Frequency

매 한 줄

"매 term frequency × inverse document frequency — 매 word's importance 의 corpus context 매 weight". Karen Spärck Jones (1972) 의 IDF formalization. 2026 매 dense retrieval (BGE, E5) 매 default 매도 매 baseline + hybrid (BM25 + dense) 의 second stage 매 still ubiquitous.

매 핵심

매 Formula

• TF: term의 doc 매 count (raw / log-normalized / frequency).
• IDF: log(N / df_t) — 매 N corpus size, df_t = doc 매 t의 contains 의 count.
• TF-IDF: TF(t,d) × IDF(t).
• L2 norm: 매 cosine 의 prepare.

매 Variants

• Raw TF / log(1+TF) / sublinear.
• IDF smoothing: log((1+N)/(1+df)) + 1.
• BM25: 매 TF saturation + length normalization 의 add.

매 응용

1. Search baseline (sklearn + scikit-learn).
2. Hybrid retrieval — 매 BM25 + dense embedding의 reciprocal-rank fuse.
3. Feature extraction 매 classical ML (logistic regression, SVM).
4. Keyword extraction (top-k tfidf).

💻 패턴

sklearn TF-IDF

from sklearn.feature_extraction.text import TfidfVectorizer

corpus = [
    "the cat sat on the mat",
    "the dog ate the bone",
    "cats and dogs are pets",
]
vec = TfidfVectorizer(stop_words="english", sublinear_tf=True, ngram_range=(1, 2))
X = vec.fit_transform(corpus)  # sparse (n_docs, n_features)
print(vec.get_feature_names_out())

Cosine search

from sklearn.metrics.pairwise import cosine_similarity

q = vec.transform(["pet animals"])
sims = cosine_similarity(q, X).flatten()
ranking = sims.argsort()[::-1]

Manual IDF (educational)

import math
from collections import Counter

def compute_idf(corpus_tokens):
    N = len(corpus_tokens)
    df = Counter()
    for tokens in corpus_tokens:
        for t in set(tokens):
            df[t] += 1
    return {t: math.log((N + 1) / (df_t + 1)) + 1 for t, df_t in df.items()}

BM25 (preferred over plain TF-IDF for IR)

from rank_bm25 import BM25Okapi

tokenized = [doc.lower().split() for doc in corpus]
bm25 = BM25Okapi(tokenized, k1=1.5, b=0.75)
scores = bm25.get_scores("pet animals".split())

Hybrid search (2026 standard)

import numpy as np

def rrf(rankings, k=60):
    scores = {}
    for ranking in rankings:
        for rank, doc_id in enumerate(ranking):
            scores[doc_id] = scores.get(doc_id, 0) + 1 / (k + rank)
    return sorted(scores, key=scores.get, reverse=True)

bm25_top = bm25.get_top_n("query".split(), corpus, n=100)
dense_top = dense_index.search("query", k=100)
final = rrf([bm25_top, dense_top])[:10]

Top keyword extraction

def top_keywords(doc_idx, vec, X, k=10):
    row = X[doc_idx].toarray().flatten()
    feats = vec.get_feature_names_out()
    top = np.argsort(-row)[:k]
    return [(feats[i], row[i]) for i in top]

Persistence

import joblib
joblib.dump((vec, X), "tfidf_index.joblib")
vec, X = joblib.load("tfidf_index.joblib")

매 결정 기준

상황	Approach
매 small corpus + interpretability	TF-IDF (sklearn)
매 medium corpus + better recall	BM25
매 semantic / paraphrase	Dense (BGE-M3, E5)
매 production search	Hybrid (BM25 + dense + RRF)
매 keyword extraction / explanation	Plain TF-IDF top-k

기본값: 매 BM25 baseline → 매 hybrid + reranker (cross-encoder) for 2026 production.

🔗 Graph

• 부모: Information Retrieval
• 변형: BM25
• 응용: Search Engine · RAG
• Adjacent: Dense Retrieval

🤖 LLM 활용

언제: 매 small corpus 매 lookup, 매 RAG 의 sparse channel, 매 explainability ("matched on 'mat', 'cat'"). 언제 X: 매 paraphrase / multilingual 매 weak — 매 dense 의 prefer.

❌ 안티패턴

• TF-IDF 만으로 production search: 매 paraphrase miss.
• No stopword / lowercasing: 매 noisy features.
• Same vectorizer not pickled: 매 train/serve mismatch.
• No length normalization: 매 long docs 의 unfair advantage (use BM25 또는 normalize).

🧪 검증 / 중복

• Verified (Spärck Jones 1972; Manning IR Book Ch.6; sklearn TfidfVectorizer 2026).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — TF-IDF formula + sklearn + BM25 + hybrid RRF