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Supervised/unsupervised learning, model selection, evaluation, and scikit-learn. Use for building classification, regression, or clustering models.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-ai-data-scientist --skill machine-learning명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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SKILL.md 표시 중
Python fundamentals, data structures, OOP, and data science libraries (Pandas, NumPy). Use when writing Python code, data manipulation, or algorithm implementation.
Q-learning, DQN, PPO, A3C, policy gradient methods, multi-agent systems, and Gym environments. Use for training agents, game AI, robotics, or decision-making systems.
ARIMA, SARIMA, Prophet, trend analysis, seasonality detection, anomaly detection, and forecasting methods. Use for time-based predictions, demand forecasting, or temporal pattern analysis.
SOC 직업 분류 기준
| name | machine-learning |
| description | Supervised/unsupervised learning, model selection, evaluation, and scikit-learn. Use for building classification, regression, or clustering models. |
| sasmp_version | 1.3.0 |
| bonded_agent | 04-machine-learning-ai |
| bond_type | PRIMARY_BOND |
Build, train, and evaluate ML models for classification, regression, and clustering.
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
# Split data
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
# Train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
probabilities = model.predict_proba(X_test)
(classification_report(y_test, predictions))
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_absolute_error, r2_score
model = GradientBoostingRegressor(n_estimators=100)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(f"MAE: {mean_absolute_error(y_test, predictions):.2f}")
print(f"R²: {r2_score(y_test, predictions):.3f}")
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
# Find optimal k (elbow method)
inertias = []
for k in range(1, 11):
km = KMeans(n_clusters=k, random_state=42)
km.fit(X)
inertias.append(km.inertia_)
plt.plot(range(1, 11), inertias, marker='o')
plt.xlabel('Number of clusters')
plt.ylabel('Inertia')
plt.show()
# Train with optimal k
kmeans = KMeans(n_clusters=5, random_state=42)
clusters = kmeans.fit_predict(X)
Classification:
Regression:
Clustering:
Classification:
from sklearn.metrics import (
accuracy_score, precision_score, recall_score,
f1_score, roc_auc_score, confusion_matrix
)
accuracy = accuracy_score(y_true, y_pred)
precision = precision_score(y_true, y_pred, average='weighted')
recall = recall_score(y_true, y_pred, average='weighted')
f1 = f1_score(y_true, y_pred, average='weighted')
roc_auc = roc_auc_score(y_true, y_pred_proba, multi_class='ovr')
Regression:
from sklearn.metrics import (
mean_absolute_error, mean_squared_error, r2_score
)
mae = mean_absolute_error(y_true, y_pred)
mse = mean_squared_error(y_true, y_pred)
rmse = np.sqrt(mse)
r2 = r2_score(y_true, y_pred)
from sklearn.model_selection import cross_val_score
scores = cross_val_score(model, X, y, cv=5, scoring='f1_weighted')
print(f"CV F1: {scores.mean():.3f} (+/- {scores.std() * 2:.3f})")
from sklearn.model_selection import GridSearchCV
param_grid = {
'n_estimators': [100, 200, 300],
'max_depth': [5, 10, 15],
'min_samples_split': [2, 5, 10]
}
grid_search = GridSearchCV(
RandomForestClassifier(),
param_grid,
cv=5,
scoring='f1_weighted',
n_jobs=-1
)
grid_search.fit(X_train, y_train)
print(f"Best params: {grid_search.best_params_}")
print(f"Best score: {grid_search.best_score_:.3f}")
# Use best model
best_model = grid_search.best_estimator_
from sklearn.preprocessing import StandardScaler, LabelEncoder
# Scaling
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Encoding
encoder = LabelEncoder()
y_encoded = encoder.fit_transform(y)
# Polynomial features
from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(degree=2)
X_poly = poly.fit_transform(X)
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
pipeline = Pipeline([
('scaler', StandardScaler()),
('classifier', RandomForestClassifier(n_estimators=100))
])
pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)