Antigravity Agent
f8b21af4be
10_Wiki/Topics 대규모 정리: - 오류 캡처/미완성 stub 문서 227개 제거 - 교차폴더 중복 43클러스터 병합 (63파일 → redirect) - 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건 - 카테고리 MOC 6개 신규 생성 - Graph 섹션 미해결 related-keyword 링크 10,058건 제거 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
160 lines
5.2 KiB
Markdown
160 lines
5.2 KiB
Markdown
---
|
||
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 |
|