LightGBM的使用

import lightgbm as lgb
from sklearn.metrics import roc_auc_score
lgb_train = lgb.Dataset(x_train,y_train)
lgb_eval = lgb.Dataset(x_test,y_test,reference = lgb_train)
params = {
        'boosting_type':'gbdt',  #提升器的类型
        'objective':'binary',   
        'metric':{'auc'},
        'num_leaves':32,
        'learning_rate':0.01,
        'feature_fraction':0.9,  #每棵树训练之前选择90%的特征
        'bagging_fraction':0.8,  #类似于feature_fraction，加速训练，处理过拟合
        'bagging_freq':5
        'verbose':0
}
gbm = lgb.train(params,
lgb_train,
num_boost_round = 4000, #number of boosting iterations,
valid_sets = lgb_eval,
verbose_eval=250,
early_stopping_rounds=50)
y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)

LightGBM Python 模块加载后的数据存在于Dataset对象中。

创建验证数据：

lgb_eval = lgb.Dataset(x_test,y_test,reference = lgb_train)

在LightGBM中，验证数据应该与训练数据一致（格式一致）

或者另一种形式：

lgb_eval = lgb_train.create_vaild(x_test,y_test)

参考：LightGBM的官方文档