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...
num_boost_round=20, 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 ax = lgb.plot_tree(g...
称为折叠 (fold) （如果 k = n， 这等价于 Leave One Out（留一） 策略），都具有相同的大小（...
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MedianAPE=25.2%,结果不太理想,我们接下来对模型进行优化。
data=lgb.Dataset(X_train,label=y_train,categorical_feature=cat_features)test_data=lgb.Dataset(X_test,label=y_test,reference=train_data)# 设置参数params={'objective':'binary','metric':'binary_logloss','boosting_type':'gbdt'}# 训练模型lgb_model=lgb.train(params,train_data,valid_sets=[test...
{ '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...
} 2.修改App.xaml.cs private void Application_Startup(object sender, StartupEventArgs e)
valid_sets=valid_data, num_boost_round=252, keep_training_booster = True ) Copy link Collaborator jmoralezcommentedApr 25, 2023 Hi. Would you mind us taking a step back to be clear on the objective? I don't fully understand what you're trying to achieve. If you want to reproduce the...
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...') ...