我们使用 eval_metrics 方法计算指定数据集的指定指标。 metrics = model.eval_metrics( data=pool1, metrics=['Logloss','AUC'], ntree_start=0, ntree_end=0, eval_period=1, plot=True ) 从可视化结果看,eval_metrics 只包含 Eval 结果曲线,我们设置了 metrics=['Logloss','AUC'] ,因此包含'Loglos...
from catboostimportPool,cv from catboost.utilsimporteval_metric from catboost.coreimportMetricVisualizer from catboostimportCatBoostClassifier from sklearn.metricsimportaccuracy_score 2. 自定义目标函数 我们可以通过CatBoost的ObjectiveFunction类来自定义目标函数。以下是一个简单的示例,我们将自定义一个目标函数,假设...
首先,我们需要导入CatBoost库以及其他可能需要的Python库。 importnumpyasnpimportcatboostascbfromcatboostimportPool, cvfromcatboost.utilsimporteval_metricfromcatboost.coreimportMetricVisualizerfromcatboostimportCatBoostClassifierfromsklearn.metricsimportaccuracy_score 2. 自定义目标函数 我们可以通过CatBoost的ObjectiveFuncti...
X_train,X_test,y_train,y_test=train_test_split(data.drop(['income'],axis=1),data['income'],random_state=10,test_size=0.3)# 配置训练参数 clf=cb.CatBoostClassifier(eval_metric="AUC",depth=4,iterations=500,l2_leaf_reg=1,learning_rate=0.1)# 类别特征索引 cat_features_index=[1,3,5,6...
return(metrics.roc_auc_score(y_train,m.predict_proba(train)[:,1]), metrics.roc_auc_score(y_test,m.predict_proba(test)[:,1])) # Parameter Tuning model = xgb.XGBClassifier() param_dist = {"max_depth": [10,30,50], "min_...
importlightgbmaslgbfromsklearnimportmetricsdefauc2(m,train,test):return(metrics.roc_auc_score(y_train,m.predict(train)),metrics.roc_auc_score(y_test,m.predict(test)))lg=lgb.LGBMClassifier(verbose=0)param_dist={"max_depth":[25,50,75],"learning_rate":[0.01,0.05,0.1],"num_leaves":[300...
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score import optuna from core.utils.log import logger from core.stock.tushare_data_provider import get_technical_factor def lazy(fullname): if fullname in sys.modules: ...
importnumpyasnpimportpandasaspdfromsklearn.model_selectionimporttrain_test_splitimportcatboostascbfromsklearn.metricsimportf1_score # 读取数据data = pd.read_csv('./adult.data', header=None)# 变量重命名data.columns = ['age','workclass','fnlwgt','education',...
metrics.roc_auc_score(y_test,m.predict_proba(test)[:,1]))# Parameter Tuning model = xgb.XGBClassifier()param_dist = {"max_depth": [10,30,50],"min_child_weight" : [1,3,6],"n_estimators": [200],"learning_rate": [0.05, 0.1,0.16],} grid_search = GridSearchCV(model, ...
评估/验证eval 导出模型dump 导入导出模型的路径model_in和model_out fmap,feature map用来导出模型 LightGBM 特点 效率和内存上的提升 直方图算法,LightGBM提供一种数据类型的封装相对Numpy,Pandas,Array等数据对象而言节省了内存的使用,原因在于他只需要保存离散的直方图,LightGBM里默认的训练决策树时使用直方图算法,XGBoost...