verbose_eval 进行n次验证后,报告metrics情况。 fobj 自定义目标函数。 feval 自定义评价标准。two parameters: preds, train_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples def f1_score_vali(preds, data_vali): labels = data_vali.get_label() preds = np.ar...
找出来x_train, y_train, x_val, y_val,eval_set = [(x_val, y_val)] 然后用fit来训练模型 Model.fit(x_train, y_train, eval_set=eval_set, early_stopping_rounds=300, eval_metric=’’,sample_weight=, verbose=100 ) Fit训练完以后,还是和调用train一样的,来predict新的,也是用的best_itera...
lgb_train1 = lgb.Dataset(train_feat1[predictors], train_feat1[u'血糖'],categorical_feature=[u'性别']) lgb_train2 = lgb.Dataset(train_feat2[predictors], train_feat2[u'血糖']) gbm = lgb.train(params, lgb_train1, num_boost_round=3000, valid_sets=lgb_train2, verbose_eval=100, feva...
train_X = my_imputer.fit_transform(train_X) test_X = my_imputer.transform(test_X) # 5.转换为Dataset数据格式 lgb_train = lgb.Dataset(train_X, train_y) lgb_eval = lgb.Dataset(test_X, test_y, reference=lgb_train) # 6.参数 params = { 'task': 'train', 'boosting_type': 'gbdt'...
'verbose': 1 # <0 显示致命的, =0 显示错误 (警告), >0 显示信息 } # 模型训练 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')
num_rounds =2000# 迭代次数watchlist = [(xgb_train,'train'), (xgb_val,'val')]# 交叉验证result = xgb.cv(plst, xgb_train, num_boost_round=200, nfold=4, early_stopping_rounds=200, verbose_eval=True, folds=StratifiedKFold(n_splits=4).split(X, y))# 训练模型并保存# early_stopping_...
early_stopping_rounds=200, verbose_eval=True, folds=StratifiedKFold(n_splits=4).split(X, y))#训练模型并保存#early_stopping_rounds 当设置的迭代次数较大时,early_stopping_rounds 可在一定的迭代次数内准确率没有提升就停止训练model = xgb.train(plst, xgb_train, num_rounds, watchlist, early_stoppi...
#与multiclass搭配 'metric': 'multi_logloss', 'num_leaves': 31, 'learning_rate': 0.05, 'feature_fraction': 0.9, 'bagging_fraction': 0.8, 'bagging_freq': 5, 'verbose': 0, 'n_jobs': 4 } num_round = 20 gbm = lgb.train(params, train, valid_sets=test, num_boost_round=num_roun...
,feature_name=features,categorical_feature=cat_feature,early_stopping_rounds=earlyStopping,verbose_eval=False,keep_training_booster=True)train_auc=m.best_score['train']['auc']valid_auc=m.best_score['valid']['auc']loss_auc=train_auc-valid_aucprint('训练集auc:{},测试集auc:{},loss_auc:{}...
verbose_eval=False, show_stdv=True) # 最优目标 loss = 1-max(cv_results['auc-mean']) print("交叉验证AUC:{},参数:{}".format(max(cv_results['auc-mean']),params0)) return {'loss': loss, 'params': params, 'status': STATUS_OK} ...