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...
[9] valid_0's l2: 0.215351 valid_0's auc: 0.809041 [10] valid_0's l2: 0.213064 valid_0's auc: 0.805953 [11] valid_0's l2: 0.211053 valid_0's auc: 0.804631 [12] valid_0's l2: 0.209336 valid_0's auc: 0.802922 [13] valid_0's l2: 0.207492 valid_0's auc: 0.802011 [14]...
params, train_set, num_boost_round=100, valid_sets=None, valid_names=None, fobj=None, feval=None, init_model=None, feature_name='auto', categorical_feature='auto', early_stopping_rounds=None, evals_result=None, verbose_eval=True, learning_rates=None, keep_training_booster=False, callbacks...
[9] valid_0's l2: 0.215351 valid_0's auc: 0.809041 [10] valid_0's l2: 0.213064 valid_0's auc: 0.805953 [11] valid_0's l2: 0.211053 valid_0's auc: 0.804631 [12] valid_0's l2: 0.209336 valid_0's auc: 0.802922 [13] valid_0's l2: 0.2074...
valid_sets=lgb_eval, early_stopping_rounds=100, verbose_eval=100) 1. 2. 3. 4. 5. 6. 模型预测 训练完成后,可对新数据进行预测,并根据需要将预测结果保存或进行后续分析。 predictions = gbm.predict(X_test, num_iteration=gbm.best_iteration) ...
valid_sets=(lgb_valid, lgb_train), valid_names=('validate','train'), early_stopping_rounds = early_stop_rounds, evals_result= results) #=== #四,评估模型 #=== printlog("step4: evaluating model ...") y_pred_train = gbm.predict(df...
model=lgb.train(params,train_data,valid_sets=[valid_data],num_boost_round=FIXED_PARAMS['num_boost_round'],early_stopping_rounds=FIXED_PARAMS['early_stopping_rounds'],valid_names=['valid'])score=model.best_score['valid']['auc']returnscore...
# 3. trainbst = lgb.train(params=config, train_set=train_data, valid_sets=[val_data]) # 4. predictlgb.predict(val_data) # lightgbm_config.json{"objective":"binary","task":"train","boosting":"gbdt","num_iterations":500,"learning_rate":0.1,"max_depth":-1,"num_leaves":64,"tree...
}print('Start training...')#训练 cv and trainmodel = lgb.train(params,lgb_train,num_boost_round=tree_num,valid_sets=lgb_eval)#训练数据需要参数列表和数据集yhat = model.predict(X_test, num_iteration=model.best_iteration) error=rmspe( np.expm1(yhat), np.expm1(y_test))returnerrordefload...
gbm=lgb.train(params,lgb_train,num_boost_round=20,valid_sets=lgb_eval,early_stopping_rounds=5)# 保存模型到文件 #gbm.save_model('model.txt')joblib.dump(lgb,'./model/lgb.pkl')# 预测数据集 y_pred=gbm.predict(X_test,num_iteration=gbm.best_iteration)# 评估模型print('The rmse of predicti...