LightGBM常用模板

LightGBM是个快速的、分布式的、高性能的基于决策树算法的梯度提升框架。可用于排序、分类、回归以及很多其他的机器学习任务中
lgb.LGBMClassifier（）用于分类，回归可用lgb.LGBMRegressor（）方法
模板一：

import lightgbm as lgb
print("LGB test")
clf = lgb.LGBMClassifier(
        boosting_type='gbdt', num_leaves=55, reg_alpha=0.0, reg_lambda=1,
        max_depth=15, n_estimators=6000, objective='binary',
        subsample=0.8, colsample_bytree=0.8, subsample_freq=1,
        learning_rate=0.06, min_child_weight=1, random_state=20, n_jobs=4
    )
clf.fit(X_train, y_train)
pre=clf.predict(testdata)

模板二：

print("starting first testing......")  
clf = lgb.LGBMClassifier(  
        boosting_type='gbdt', num_leaves=50, reg_alpha=0.0, reg_lambda=1,  
        max_depth=-1, n_estimators=1500, objective='binary',  
        subsample=0.7, colsample_bytree=0.7, subsample_freq=1,  
        learning_rate=0.05, min_child_weight=50, random_state=2018, n_jobs=100  
    )  
clf.fit(X_train, y_train, eval_set=[(X_train, y_train)], eval_metric='auc',early_stopping_rounds=1000)  
pre1=clf.predict(X_test)

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