refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-feature-engineering
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title: Feature Engineering
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [FE, feature engineering, target encoding, feature crossing, feature store]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.98
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verification_status: applied
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tags: [machine-learning, feature-engineering, preprocessing, target-encoding, feature-store]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: pandas / scikit-learn / Featuretools / Feast
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---
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# Feature Engineering
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## 매 한 줄
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> **"매 raw data 의 model-ready feature 의 transform"**. 매 numerical scaling, 매 categorical encoding, 매 datetime, 매 text, 매 interaction. 매 modern: 매 deep learning 의 자동 학습 BUT 매 tabular 의 still 매 critical. 매 feature store (Feast) for production.
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## 매 핵심
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### 매 numerical
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- **Scaling**: standard, minmax, robust.
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- **Power**: log, Box-Cox, Yeo-Johnson.
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- **Bin / discretize**: equal-width, quantile.
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- **Polynomial / interaction**.
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### 매 categorical
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- **One-hot**: 매 low cardinality.
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- **Label / ordinal**: 매 ordered.
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- **Target encoding** (mean): 매 high cardinality + leakage care.
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- **Hashing trick**: 매 fixed dim.
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- **Embedding**: 매 NN.
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### 매 datetime
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- **Cyclic** (sin/cos for hour/day).
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- **Lag features** (time series).
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- **Rolling stats**.
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- **Holiday / weekend**.
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### 매 text
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- **Bag of words / TF-IDF**.
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- **N-grams**.
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- **Embeddings** (BERT, sentence-transformers).
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- **LLM features**.
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### 매 응용
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1. **Tabular ML**: 매 critical.
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2. **Time series**: 매 lag / rolling.
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3. **NLP**: 매 embed.
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4. **Graph**: 매 graph features (node degree, ...).
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## 💻 패턴
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### Standard scale
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```python
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from sklearn.preprocessing import StandardScaler
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scaler = StandardScaler().fit(X_train)
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X_scaled = scaler.transform(X_test)
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```
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### Cyclic datetime
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```python
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import numpy as np
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def encode_cyclic(value, max_value):
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return np.sin(2 * np.pi * value / max_value), np.cos(2 * np.pi * value / max_value)
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df['hour_sin'], df['hour_cos'] = encode_cyclic(df.hour, 24)
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df['dow_sin'], df['dow_cos'] = encode_cyclic(df.day_of_week, 7)
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```
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### Target encoding (with smoothing)
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```python
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def target_encode(train, test, col, target, smoothing=10):
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global_mean = train[target].mean()
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agg = train.groupby(col)[target].agg(['mean', 'count'])
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smoothed = (agg['count'] * agg['mean'] + smoothing * global_mean) / (agg['count'] + smoothing)
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return test[col].map(smoothed).fillna(global_mean)
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```
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### Out-of-fold target encoding (no leakage)
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```python
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from sklearn.model_selection import KFold
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def oof_target_encode(X, y, col, n_folds=5):
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enc = np.zeros(len(X))
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for tr_idx, val_idx in KFold(n_folds, shuffle=True).split(X):
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means = X.iloc[tr_idx].groupby(col).apply(lambda g: y.iloc[g.index].mean())
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enc[val_idx] = X.iloc[val_idx][col].map(means).fillna(y.iloc[tr_idx].mean())
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return enc
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```
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### Lag features (time series)
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```python
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def lag_features(df, target_col, lags=[1, 7, 30]):
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for lag in lags:
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df[f'{target_col}_lag{lag}'] = df[target_col].shift(lag)
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return df
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```
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### Rolling stats
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```python
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df['amt_roll_mean_7'] = df.groupby('user_id')['amount'].transform(
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lambda s: s.rolling(7, min_periods=1).mean()
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)
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df['amt_roll_std_7'] = df.groupby('user_id')['amount'].transform(
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lambda s: s.rolling(7, min_periods=1).std()
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)
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```
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### Aggregation per group
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```python
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agg = df.groupby('user_id')['amount'].agg(['mean', 'std', 'max', 'count']).reset_index()
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df = df.merge(agg, on='user_id', suffixes=('', '_agg'))
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```
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### Interaction (cross)
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```python
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from sklearn.preprocessing import PolynomialFeatures
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poly = PolynomialFeatures(degree=2, interaction_only=True, include_bias=False)
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X_inter = poly.fit_transform(X[['age', 'income']])
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```
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### Hashing trick (high-card)
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```python
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from sklearn.feature_extraction import FeatureHasher
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h = FeatureHasher(n_features=2**18, input_type='string')
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X_hashed = h.transform([str(row.user_id) for _, row in df.iterrows()])
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```
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### Featuretools (automated)
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```python
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import featuretools as ft
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es = ft.EntitySet('shop')
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es.add_dataframe('orders', df=orders_df, index='order_id', time_index='date')
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es.add_dataframe('users', df=users_df, index='user_id')
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es.add_relationship('users', 'user_id', 'orders', 'user_id')
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features, defs = ft.dfs(entityset=es, target_dataframe_name='users',
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agg_primitives=['mean', 'sum', 'count'])
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```
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### Feast feature store (production)
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```python
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from feast import FeatureStore, Entity, FeatureView, Field
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from feast.types import Float32, Int64
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user = Entity(name='user', value_type=Int64)
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user_features = FeatureView(
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name='user_features',
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entities=[user],
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ttl=timedelta(days=1),
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schema=[Field(name='ltv', dtype=Float32), Field(name='tenure', dtype=Int64)],
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source=BigQuerySource(table='proj.user_features'),
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)
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store = FeatureStore(repo_path='.')
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features = store.get_online_features(features=['user_features:ltv'], entity_rows=[{'user': 1}])
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```
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### LLM-as-feature
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```python
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def llm_sentiment(text, llm):
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return llm.classify(text, ['positive', 'neutral', 'negative'])
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df['llm_sentiment'] = df['review'].apply(lambda t: llm_sentiment(t, llm))
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```
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### Embedding feature
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer('all-MiniLM-L6-v2')
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df['title_emb'] = list(model.encode(df['title'].tolist()))
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```
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### Pipeline (sklearn)
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```python
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from sklearn.pipeline import Pipeline
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from sklearn.compose import ColumnTransformer
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preprocessor = ColumnTransformer([
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('num', StandardScaler(), num_cols),
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('cat', OneHotEncoder(handle_unknown='ignore'), cat_cols),
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])
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pipe = Pipeline([('prep', preprocessor), ('model', xgb.XGBClassifier())])
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pipe.fit(X_train, y_train)
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```
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### Anti-leakage
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```python
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def split_then_fit(X, y):
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"""매 ALWAYS split first 의 fit transformer."""
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X_tr, X_val, y_tr, y_val = train_test_split(X, y)
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scaler = StandardScaler().fit(X_tr) # 매 train only
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X_tr_s = scaler.transform(X_tr)
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X_val_s = scaler.transform(X_val) # 매 val 의 transform only
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return X_tr_s, X_val_s, y_tr, y_val
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Numerical + tree | Often raw OK |
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| Numerical + linear | Scale (Standard) |
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| Cardinality < 50 | One-hot |
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| Cardinality > 50 | Target encode (OOF) or hash |
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| Time series | Lag + rolling |
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| Production | Feature store (Feast) |
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| Auto | Featuretools / Tsfresh |
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**기본값**: 매 manual + 매 OOF target encode + 매 cyclic datetime + 매 leakage prevent + 매 production = feature store.
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## 🔗 Graph
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- 부모: [[Machine-Learning]] · [[Data-Preprocessing]]
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- 변형: [[Target-Encoding]] · [[Embeddings]]
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- 응용: [[Feature-Store]] · [[Feast]]
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- Adjacent: [[Exploratory-Data-Analysis]]
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## 🤖 LLM 활용
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**언제**: 매 tabular ML. 매 time series. 매 production system.
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**언제 X**: 매 deep learning end-to-end (image, text).
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## ❌ 안티패턴
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- **Fit on full data**: 매 leakage.
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- **Naive target encode**: 매 leakage.
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- **No cyclic datetime**: 매 RNN-only.
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- **Skip feature store**: 매 prod / train skew.
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- **Over-engineer for tree**: 매 little gain.
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## 🧪 검증 / 중복
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- Verified (Kuhn Feature Engineering, Kaggle competitions, Feast docs).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-04-20 | Auto-reinforced |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — categorical / time / interaction + 매 OOF / Featuretools / Feast code |
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