loss_function,损失函数,包括Logloss,RMSE,MAE,CrossEntropy,回归任务默认RMSE,分类任务默认Logloss eval_metric,优化目标,包括RMSE,Logloss,MAE,CrossEntropy,Recall,Precision,F1,Accuracy,AUC,R2 fit函数参数: X,输入数据数据类型可以是:list; pandas.DataFrame; pandas.Series y=None cat_features=None,用于处理分类...
lightGBM伪代码 fromsklearn.metricsimportlog_loss,roc_auc_score# Set parametersparams={'boosting_type':'gbdt','objective':'binary','metric':{'binary_logloss','auc'},'num_leaves':31,'learning_rate':0.05,'feature_fraction':0.9,'bagging_fraction':0.8,'bagging_freq':5,'verbose':0}print('S...
#xgboostparameters = {'max_depth':7,'eta':1,'silent':1,'objective':'binary:logistic','eval_metric':'auc','learning_rate':.05}# lightgbmparam = {'num_leaves':150,'objective':'binary','max_depth':7,'learning_rate':.05,'max_bin':200} param['metric'] = ['auc','binary_logloss...
lgb_train = lgb.Dataset(X_train, y_train, free_raw_data=False) lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train,free_raw_data=False) ### 设置初始参数--不含交叉验证参数 print('设置参数') params = { 'boosting_type': 'gbdt', 'objective': 'binary', 'metric': 'auc',...
lgb_eval = lgb.Dataset(test_X, test_y, reference=lgb_train) # 6.参数 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 ...
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train,free_raw_data=False) ### 设置初始参数--不含交叉验证参数 print('设置参数') params = { 'boosting_type': 'gbdt', 'objective': 'binary', 'metric': 'auc', 'nthread':4, ...
'metric': 'auc', 'is_unbalance': True, 'boost_from_average': False, } train_data = lgb.Dataset(data=train,label=y_train) test_data = lgb.Dataset(data=test,label=y_test) num_round = 5000 clf = lgb.train(param, train_data, num_round, valid_sets = test_data, verbose_eval=250,...
= LightGBMRanker( labelCol=label_col, featuresCol=features_col, groupCol=query_col, predictionCol="preds", leafPredictionCol="leafPreds", featuresShapCol="importances", repartitionByGroupingColumn=True, numLeaves=32, numIterations=200, evalAt=[1,3,5], metric="ndcg", dataTransferMode="bulk"...
LightGBMRanker( labelCol=label_col, featuresCol=features_col, groupCol=query_col, predictionCol="preds", leafPredictionCol="leafPreds", featuresShapCol="importances", repartitionByGroupingColumn=True, numLeaves=32, numIterations=200, evalAt=[1, 3, 5], metric="ndcg", dataTransferMode="bulk"...
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) probs = gbm.predict(X_test, num_iteration=gbm.best_iteration) # 输出的是概率结果 fpr, tpr, thresholds = roc_curve(y_test, probs) st.write('---') st.write('Confusion Matrix:') st.write(confusion_...