valid_sets=lgb_eval) print('逐步调整学习率完成第 20-30 轮训练...') # 调整其他超参数 gbm = lgb.train(params, lgb_train, num_boost_round=10, init_model=gbm, valid_sets=lgb_eval, callbacks=[lgb.reset_parameter(bagging_fraction=[0.7] * 5 + [0.6] * 5)]) print('逐步调整bagging比率...
gbm = lgb.train(params, lgb_train, num_boost_round=20, valid_sets=lgb_eval, early_stopping_rounds=5) # 模型保存 gbm.save_model('model.txt') # 模型加载 gbm = lgb.Booster(model_file='model.txt') # 模型预测 y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration) # 模型评...
m1 = LGB.train(params,lgb_train,num_boost_round=2000, valid_sets=[lgb_train,lgb_eval],callbacks=callback) #预测数据集 y_pred = m1.predict(X_test) #评估模型 regression_metrics(y_test,y_pred) 基础模型的训练过程与评估结果如下: 基础模型的平均绝对百分比误差MAPE=105%,绝对百分比误差中位数Med...
AI检测代码解析 importlightgbmaslgbimportpandasaspdfromsklearn.model_selectionimporttrain_test_split# 读取数据集data=pd.read_csv('data.csv')# 将类别特征转换为category类型cat_features=['category1','category2']forfeatureincat_features:data[feature]=data[feature].astype('category')# 划分训练集和测试...
valid_sets=lgb_eval, early_stopping_rounds=5) 三、使用plot_tree绘制 LightGBM提供了plot_tree函数,该函数可以用于绘制出模型中的特定树。 使用plot_tree函数 通过lgb.plot_tree函数来绘制决策树。 import matplotlib.pyplot as plt 选择树的编号 tree_index = 0 ...
{ 'objective':'multiclass', 'num_class': 3, } gbm = lgb.train(params, lgb_train, num_boost_round=20, valid_sets=lgb_test, callbacks=[lgb.early_stopping(stopping_rounds=5)]) pred = gbm.predict(x_multi_test) print(f'lgbm *** 原生接口 f1_score {f1_score(y_multi_test,np.argmax...
model.LGBMRegressor.fit(x_train，y_train)和lightgbm.train(train_data，valid_sets = test_data)有...
} 2.修改App.xaml.cs private void Application_Startup(object sender, StartupEventArgs e)
valid_sets=valid_sets, valid_names=eval_names, 614 early_stopping_rounds=early_stopping_rounds, /opt/conda/lib/python3.8/site-packages/lightgbm/engine.py in train() 226 # construct booster 227 try: --> 228 booster = Booster(params=params, train_set=train_set) 229 if is_valid_contain_tra...
gbm = lgb.train(params,lgb_train,num_boost_round=20,valid_sets=lgb_eval,early_stopping_rounds=5) # 训练数据需要参数列表和数据集 print('Save model...') gbm.save_model('model.txt') # 训练后保存模型到文件 print('Start predicting...') ...