// python环境valpythonExec:String= sparkConf.get("spark.pyspark.python")// 训练参数varparamMap:Map[String,Any] =Map()// 忽略其他参数设置// ...// tracker配置paramMap = paramMap + ("tracker_conf"->newTrackerConf(0,"python", pythonExec = pythonExec))// xgb分类器valclassifier:XGBoostCla...
xgb_model=xgb.XGBClassifier()lr_model=LogisticRegression()dt_model=DecisionTreeClassifier()# 创建集成模型 ensemble_model=VotingClassifier(estimators=[('xgb',xgb_model),('lr',lr_model),('dt',dt_model)],voting='hard')# 训练集成模型 ensemble_model.fit(X_train,y_train)# 在测试集上评估模型 y...
rfc = RandomForestClassifier() rfc.fit(X_train, y_train) rfc.score(X_test, y_test) xgbc = XGBClassifier() xgbc.fit(X_train, y_train) xgbc.score(X_test, y_test) 1. 2. 3. 4. 5. 6. 7. 8. class RandomForestClassifier(ForestClassifier): """A random forest classifier. A rand...
Building an XGBoost classifier Changing between Sklearn and native APIs of XGBoost Let’s get started! Run and edit the code from this tutorial online Run code XGBoost Installation You caninstall XGBoost like any other library through pip. This method of installation will also include support for...
File "/volumes/code/autoai/models/classifier.py", line 8, in <module> from eli5 import explain_prediction File "/volumes/dependencies/lib/python3.6/site-packages/eli5/__init__.py", line 53, in <module> from .xgboost import ( File "/volumes/dependencies/lib/python3.6/site-packages/eli5/...
(gbc,x_train, y_train, x_test) # Gradient Boost svc_oof_train, svc_oof_test = get_oof(sv,x_train, y_train, x_test) # Support Vector Classifier x_train = np.concatenate(( rf_oof_train, ada_oof_train, gb_oof_train, svc_oof_train), axis=1) x_test = np.concatenate(( rf_...
lg= lgb.LGBMClassifier(silent=False) param_dist= {"max_depth": [25,50, 75],"learning_rate": [0.01,0.05,0.1],"num_leaves": [300,900,1200],"n_estimators": [200] } grid_search= GridSearchCV(lg, n_jobs=-1, param_grid=param_dist, cv = 3, ...
②、经上传Kaggle比较后,三大模型中表现最好的为朴素贝叶斯模型,为尝试更高的精确度,笔者考虑采用集成学习(Stacking Classifier)进一步提高分类能力。 针对目前存在的第一类问题,在此引入SMOTE过采样方法,其定义如下: SMOTE全称是Synthetic Minority Oversampling Technique,即合成少数类过采样技术,它是基于随机过采样算法的...
fromcatboostimportCatBoostClassifier fromsklearn.preprocessingimportLabelEncoder importgc importos fromsklearn.metricsimportprecision_score,recall_score,f1_score importxgboostasxgb importwarnings warnings.filterwarnings("ignore") importjieba importmatplotlib.pyplotasplt ...
xgb_classifier=SparkXGBClassifier(max_depth=5,missing=0.0,validation_indicator_col='isVal',weight_col='weight',early_stopping_rounds=1,eval_metric='logloss',num_workers=2) Cell 8: The classifier is trained on the training data xgb_clf_model=xgb_classifier.fit(df_train) ...