3. Applied Predictive Modeling [1] Modeling(Boost)

 

Modeling

Applied Predictive Modeling  

 

 

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1. XGBoost

from xgboost.sklearn import XGBModel
from xgboost import XGBRegressor
pipe = make_pipeline(
    OrdinalEncoder(), 
    XGBRegressor()
)

pipe.fit(X_train, y_train)
print('훈련 R^2: ', pipe.score(X_train, y_train))
print('TEST R^2: ', pipe.score(X_test, y_test))

print('\n훈련 MAE: ', mean_absolute_error(pipe.predict(X_train), y_train))
print('TEST MAE: ', mean_absolute_error(pipe.predict(X_test), y_test))

encoder = OrdinalEncoder()
X_train_encoded = encoder.fit_transform(X_train)
X_test_encoded = encoder.transform(X_test)

model = XGBRegressor(
    n_estimators=3000,
    max_depth=9,
    learning_rate=0.2,
)

eval_set = [(X_train_encoded, y_train), 
            (X_test_encoded, y_test)]

model.fit(X_train_encoded, y_train, 
          eval_set=eval_set,
          early_stopping_rounds=50
         )

 

2. LightGBM

from lightgbm import LGBMRegressor
pipe = make_pipeline(
    OrdinalEncoder(), 
    LGBMRegressor()
)

pipe.fit(X_train, y_train)
print('훈련 R^2: ', pipe.score(X_train, y_train))
print('TEST R^2: ', pipe.score(X_test, y_test))

print('\n훈련 MAE: ', mean_absolute_error(pipe.predict(X_train), y_train))
print('TEST MAE: ', mean_absolute_error(pipe.predict(X_test), y_test))

encoder = OrdinalEncoder()
X_train_encoded = encoder.fit_transform(X_train)
X_test_encoded = encoder.transform(X_test)

model = LGBMRegressor(
    n_estimators=3000,
    max_depth=9,
    learning_rate=0.2,
)

eval_set = [(X_train_encoded, y_train), 
            (X_test_encoded, y_test)]

model.fit(X_train_encoded, y_train, 
          eval_set=eval_set,
          early_stopping_rounds=50
         )