9.eval_metric: 10.verbose: 11.callbacks: 12.init_model: 13.pre_partition: LGBMRegressor.predict参数 1. X 2. num_iteration (n_iter_no_change) 3. raw_score 4. pred_leaf 5. pred_contrib 6. kwargs 二、LightGBM原生接口 基本设置 1.boosting_type: 2.objective: 3.metric: 数据处理与采样 ...
learning_rate:表示学习率,用于减少梯度的级别; eval_metric:表示用于过度拟合检测和最佳模型选择的度量标准; depth:表示树的深度; subsample:表示数据行的采样率,不能在贝叶斯增强类型设置中使用; l2_leaf_reg:表示成本函数的L2规则化项的系数; random_strength:表示在选择树结构时用于对拆分评分的随机量,使用此参数...
y_train, eval_set=[(X_test, y_test)], eval_metric="binary_logloss", early_stopping_rounds=100, callbacks=[ LightGBMPruningCallback(trial,"binary_logloss") ], ) preds = model.predict_proba(X_test) preds = model.predict_proba(X_test)# 优化指标logloss最小cv_scores[idx] = log_loss(...
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','objective':'regression','metric':{...
eval_metric='auc', nthread=4, is_unbalance = True, ) model.fit(train_all,y_train_all) # 查看在测试集上的效果 y_pred = model.predict_proba(test)[:,1] auc_score = roc_auc_score(y_test, y_pred) auc_score # 0.7535911207490711 ...
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, \ ...
"eval_metric":'rmse', "eval_set": [(X_test,y_test)], 'eval_names': ['valid'], 'verbose':100, 'feature_name':'auto', 'categorical_feature':'auto' } X_test.columns=["".join (c if c.isalnum() else "_"forcinstr(x))forxinX_test.columns] ...
eval_class_weight=None, eval_init_score=None, eval_metric=None, early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None): """Docstring is inherited from the LGBMModel.""" _LGBMAssertAllFinite(y) ...
deffeval_func(preds,train_data):#Defineaformulathatevaluatestheresultsreturn('feval_func_name',eval_result,False) 使用这个函数作为参数: print('Start training...')lgb_train=lgb.train(...,metric=None,feval=feval_func) 注意:要使用feval函数代替度量,您应该设置度量参数 metric “None”。
lgb_eval=lgb.Dataset(X_test,y_test,reference=lgb_train)# 创建验证数据 # 将参数写成字典下形式 params={'task':'train','boosting_type':'gbdt',# 设置提升类型'objective':'regression',# 目标函数'metric':{'l2','auc'},# 评估函数'num_leaves':31,# 叶子节点数'learning_rate':0.05,# 学习速...