[Side Project After Work] Big Data Analysis Certification Practical Exam (Type 1, 2, 3)

Task 2: What model are you planning to prepare?
1. Random Forest Classification/Regression
2. I recommend lightGBM classification/regression.

lightGBM is
This is a popular boosting model along with XGBoost.
Training and prediction speed are faster than XGBoost.
At the time of video production, lightGBM was not supported, but it is now available even in testing environments.

XGBoost runs well, but sometimes warnings occur. I recommend preparing lightGBM instead of XGBoost.

####### 분류 ####### import lightgbm as lgb model = lgb.LGBMClassifier() model.fit(X_train, y_train) y_pred = model.predict(X_test) # y_pred = model.predict_proba(X_test) #평가기준 roc-auc일 때 ####### 회귀 ####### import lightgbm as lgb model = lgb.LGBMRegressor() model.fit(X_train, y_train) y_pred = model.predict(X_test) # 하이퍼파라미터 튜닝은 xgboost(작업형2 모의고사3 강의)과 동일하게 사용하면 됩니다. # 예: max_depth=5, n_estimators=600, learning_rate=0.01