import numpy as np import random import lightgbm as lgb from sklearn import datasets, metrics, model_selection rng = np.random.RandomState(2016) X, y = datasets.make_classification(n_samples=10000, n_features=100) x_train, x_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.1, random_state=1) lgb_model = lgb.LGBMClassifier(n_estimators=100).fit(x_train, y_train, [(x_test, y_test)], eval_metric="auc")
