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比率完成第 30-40 轮训练...') 1.
X\_train=df\_train.drop(0,axis=1).values X\_test=df\_test.drop(0,axis=1).values# 构建lgb中的Dataset格式lgb\_train=lgb.Dataset(X\_train,y\_train)lgb\_eval=lgb.Dataset(X\_test,y\_test,reference=lgb\_train)# 敲定好一组参数params={'task':'train','boosting\_type':'gbdt','obje...
'min_sum_hessian_in_leaf': 100, 'ndcg_eval_at': [1, 3, 5, 10], 'sparse_threshold': 1.0, 'device': 'cpu' } print("***") t0 = time.time() gbm = lgb.train(params, train_set=dtrain, num_boost_round=10, valid_sets=None, valid_names=None, fobj=None, feval=None, init_m...
简单来说,NDCG越高,排序算法越好。 ndcg_eval_at参数 📋 ndcg_eval_at参数指定了要评估的TOP N的NDCG。也就是说,你希望看到排序列表中前多少个项目的NDCG表现。 lambdarank目标函数 🐎 'lambdarank'实际上是lambdaMART,它的预测结果是相关性得分,而不是概率。这些分数可以直接用于对每个组或查询中的项目进行排...
clf = cb.CatBoostClassifier(eval_metric="AUC", depth=10, iterations= 500, l2_leaf_reg= 9, learning_rate= 0.15) clf.fit(train,y_train) auc(clf, train, test) With Categorical features clf = cb.CatBoostClassifier(eval_metric="AUC",one_hot_max_size=31, \ ...
loss_function=metrics_dict[eval_metric]['catboost_metric_name']) model.fit(X_train, y_train, eval_set=(X_valid, y_valid), cat_features=[], use_best_model=True, verbose=False) y_pred_valid = model.predict(X_valid) y_pred = model.predict(X_test) ...
importlightgbmaslgbimporttimedtrain=lgb.Dataset('higgs.csv')params={'max_bin':63,'num_leaves':255,'learning_rate':0.1,'tree_learner':'serial','task':'train','is_training_metric':'false','min_data_in_leaf':1,'min_sum_hessian_in_leaf':100,'ndcg_eval_at':[1,3,5,10],'sparse_th...
赛题以金融风控中的个人信贷为背景,要求选手根据贷款申请人的数据信息预测其是否有违约的可能,以此判断是否通过此项贷款,这是一个典型的分类问题。通过这道赛题来引导大家了解金融风控中的一些业务背景,解决实际问题,帮助竞赛新人进行自我练习、自我提高。
eval_set=[ (valid_x[FEATURES].astype('float32'), valid_y['correct']) ], verbose=0) clf.save_model(f'Cat_question{q}_fold{i}.cbm') models[f'{grp}_{i}_{q}'] = clf oof.loc[valid_users, q-1] = clf.predict_proba(valid_x[FEATURES].astype('float32'))[:,1] ...
classifier = LGBMClassifier() classifier.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=5) [1] valid_0's binary_logloss: 0.635586 Training until validation scores don't improve for 5 rounds [2] valid_0's binary_logloss: 0.587293 [3] valid_0's binary_...