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...
import xgboost as xgb from sklearn import metrics from sklearn.model_selection import GridSearchCV def auc(m, train,test): return(metrics.roc_auc_score(y_train,m.predict_proba(train)[:,1]), metrics.roc_auc_score(y_test,m.predic...
import xgboost as xgb from sklearn import metrics def auc(m, train, test): 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": ...
eval_set: catboost.Pool or list, optional (default=None). A list of (X, y) tuple pairs to use as a validation set for early-stopping metric_period: int. Frequency of evaluating metrics. verbose: bool or int. If verbose is bool, then if set to True, logging_level is set to Verbose...
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'Logloss'和'AUC'两条评估曲线。 print('AUC values:') print(np.array(metrics['AUC'])) 特征重要性 使用模型自带的get_feature_importance方法。 model.get_feature_importance(prettified=True) 使用...
from sklearn.metricsimportf1_score,roc_auc_score,accuracy_scoreimportplotly.graph_objsasgoimportplotly.expressaspx defprintlog(info):nowtime=datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')print("\n"+"==="*8+"%s"%nowtime)print(info+'...\n\n')#===# 一,准备数据 #===...
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',...
You can easily select evaluation metric by setting eval_metric in AutoML() constructor: automl = AutoML(mode="Compete", eval_metric="f1") automl.fit(X, y) You can check details of different metrics implementation in this file: link to metric.py....
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...
importnumpyasnpimportcatboostascbfromcatboostimportPool,cvfromcatboost.utilsimporteval_metricfromcatboost.coreimportMetricVisualizerfromcatboostimportCatBoostClassifierfromsklearn.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...