docs(10_Wiki): Topic_Business/General/Graphic/Programming을 Topics/ 하위로 이동

최상위 10_Wiki/Topic_*였던 4개 카테고리 폴더를 10_Wiki/Topics/Topic_* 로 재배치.
콘텐츠 변경 없음(순수 폴더 이동) — Topics/ 하위 나머지 폴더는 이미 지난 커밋에서
전부 정리된 상태(잔존 항목은 에이전트 운영 상태 및 사용자가 보존을 요청한
업데이트0615/무제 3.canvas 뿐).

10_Wiki/Topics/Topic_Programming/AI_and_ML/One-Hot-Encoding.md

@@ -0,0 +1,159 @@
+---
+id: wiki-2026-0508-one-hot-encoding
+title: One-Hot Encoding
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [One-Hot, OHE, Indicator-Encoding, Dummy-Encoding]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.95
+verification_status: applied
+tags: [feature-engineering, categorical, preprocessing, sklearn, pandas]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: python
+  framework: sklearn-pandas
+---
+
+# One-Hot Encoding
+
+## 매 한 줄
+> **"매 categorical value → orthogonal binary vector"**. One-hot encoding 은 K 개 category 를 K 개 0/1 column 으로 펼치는 매 가장 단순한 categorical → numeric 변환. 매 linear model / tree-based model 의 default, 그러나 high-cardinality 에서는 target / hash encoding 으로 교체.
+
+## 매 핵심
+
+### 매 정의
+- category set `{A, B, C}` → vectors `(1,0,0), (0,1,0), (0,0,1)`.
+- ordinal encoding (0,1,2) 와 달리 **순서 가정 없음**.
+- linear / kernel model 의 가정 (numeric distance) 을 깨지 않음.
+
+### 매 dummy variable trap
+- K columns → 1 redundant (sum=1 의 collinearity).
+- linear regression 의 unregularized 경우 → drop_first=True.
+- tree / regularized model (Lasso, Ridge) → 매 전체 K 유지 가능.
+
+### 매 cardinality 의 문제
+- high-cardinality (>50): sparse matrix 폭발, leak 위험.
+- 대안: target / mean encoding, hashing trick, embedding.
+
+### 매 응용
+1. tabular ML 의 categorical preprocessing.
+2. NLP token → vocab vector (sparse).
+3. RL action / state space 의 discrete encoding.
+
+## 💻 패턴
+
+### sklearn OneHotEncoder
+```python
+from sklearn.preprocessing import OneHotEncoder
+import numpy as np
+
+X = np.array([["red"], ["blue"], ["green"], ["red"]])
+enc = OneHotEncoder(sparse_output=False, handle_unknown="ignore")
+enc.fit(X)
+print(enc.transform([["red"], ["yellow"]]))
+# [[0. 0. 1.]
+#  [0. 0. 0.]]   <- unknown -> all zeros
+print(enc.get_feature_names_out())   # ['x0_blue' 'x0_green' 'x0_red']
+```
+
+### pandas get_dummies
+```python
+import pandas as pd
+df = pd.DataFrame({"color": ["red", "blue", "green", "red"]})
+ohe = pd.get_dummies(df, columns=["color"], drop_first=True, dtype=int)
+#    color_green  color_red
+# 0            0          1
+# 1            0          0
+# 2            1          0
+# 3            0          1
+```
+
+### ColumnTransformer (production pipeline)
+```python
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+from sklearn.pipeline import Pipeline
+from sklearn.linear_model import LogisticRegression
+
+pre = ColumnTransformer([
+    ("num", StandardScaler(), ["age", "income"]),
+    ("cat", OneHotEncoder(handle_unknown="ignore"), ["city", "plan"]),
+])
+pipe = Pipeline([("pre", pre), ("clf", LogisticRegression(max_iter=1000))])
+pipe.fit(X_train, y_train)
+```
+
+### Sparse matrix 의 high-cardinality
+```python
+enc = OneHotEncoder(sparse_output=True, handle_unknown="ignore")
+X_sparse = enc.fit_transform(df[["zip_code"]])   # 40k columns sparse
+# scipy.sparse.csr_matrix — memory-efficient
+```
+
+### vs label encoding (decision)
+```python
+from sklearn.preprocessing import LabelEncoder, OrdinalEncoder
+# DON'T: feed LabelEncoder output to linear model
+le = LabelEncoder()
+y = le.fit_transform(["red", "blue", "green"])   # [2, 0, 1] — fake order!
+
+# DO: OrdinalEncoder when order is real
+oe = OrdinalEncoder(categories=[["low", "med", "high"]])
+```
+
+### Frequency / target encoding (high-cardinality 대안)
+```python
+import category_encoders as ce
+te = ce.TargetEncoder(cols=["city"], smoothing=10)
+X_tr = te.fit_transform(X_train, y_train)
+X_te = te.transform(X_test)
+```
+
+### Hashing trick (constant memory)
+```python
+from sklearn.feature_extraction import FeatureHasher
+h = FeatureHasher(n_features=256, input_type="string")
+X_h = h.transform([["zip=" + z] for z in df["zip_code"]])
+```
+
+## 매 결정 기준
+| cardinality | model | encoding |
+|---|---|---|
+| <10 | any | one-hot |
+| 10–50 | linear / NN | one-hot or embedding |
+| 50–1000 | tree | target / frequency |
+| >1000 | any | hashing / embedding |
+| 매 ordinal | any | OrdinalEncoder |
+
+**기본값**: `OneHotEncoder(handle_unknown="ignore")` in ColumnTransformer.
+
+## 🔗 Graph
+- 부모: [[Feature Engineering|Feature-Engineering]]
+- 변형: [[Target-Encoding]]
+- 응용: [[Logistic-Regression-Foundations|Logistic-Regression]]
+- Adjacent: [[Sparse-Matrix]] · [[Curse-of-Dimensionality]]
+
+## 🤖 LLM 활용
+**언제**: 매 quick prototype, low-cardinality categorical, linear / tree baseline.
+**언제 X**: 매 high-cardinality (>1000), text tokens (use embedding), online learning with new categories.
+
+## ❌ 안티패턴
+- **Train-only fit**: test set 의 unseen category 에 crash → `handle_unknown="ignore"`.
+- **Drop-first with regularized model**: 불필요한 정보 손실.
+- **OHE on high-cardinality without sparse**: memory blowup.
+- **LabelEncoder for features**: fake ordinal 강제, linear model 망가짐.
+- **Leak via target encoding without fold**: target encoding 사용 시 K-fold 필수.
+
+## 🧪 검증 / 중복
+- Verified (sklearn 1.4 docs, pandas 2.2 docs).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — sklearn/pandas patterns + cardinality decision matrix |