# 需要导入模块: from xgboost import XGBClassifier [as 别名]# 或者: from xgboost.XGBClassifier importfit[as 别名]deffeature_selection(model, X_train, X_test, y_train, y_test, eval_metric='auc'):thresholds = [thresforthresinsorted(model.feature_importances_)ifthres !=0]# Use feat. with >...
我相信你把目标函数和目标函数(obj作为参数)搞混了,xgboost文档有时会让人很困惑。
Consequently, my expectation is that alsoxgb.train()andXGBClassifier.fit()will yield the same results (asXGBClassifieris just a wrapper aroundxgb.train()). However, as following minimal code example shows the output is not the same. import numpy as np import sklearn.datasets as datasets from ...
['label'],stratify=dataset['label'],test_size=0.2,random_state=2)xg_instance=XGBClassifier(n_jobs=3,random_state=10,booster='gbtree',objective='binary:logitraw',eval_metric='auc',max_depth=3,learning_rate=0.2,n_estimators=130,reg_alpha=0.32)xg_instance.fit(X_train,y_train)xg_instance....
xgb1.set_params(n_estimators=cvresult.shape[0]) xgb1.fit(X_train, y_train, eval_metric='auc') output = xgb1.predict_proba(X_test)[:,1] submission = pd.DataFrame({"ID":y_test,"TARGET":output}) submission.to_csv("submission.csv", index=False)...
Why calling fit resets custom objective function in XGBClassifier? I have tried to setup XGBoost sklearn API XGBClassifier to use custom objective function (brier) according to the documentation: .. note:: Custom objective function A custom objective ... ...
GridSearchCV, KFold,RandomizedSearchCV, train_test_split import xgboost as xgb cancer = load_breast_cancer() X = cancer.data y = cancer.target xgb_model = xgb.XGBClassifier(objective="binary:logistic", random_state=45) xgb_model.fit(X, y) pickle.dump(xgb_model, open("xgb_m...
fit(dataset) Fit gradient boosting classifier For more details on this function, see xgboost.XGBClassifier.fit predict(dataset) Predict with X For more details on this function, see xgboost.XGBClassifier.predict predict_proba(dataset[, output_cols_prefix]) Predict the probability of...
fit(self.X, self.target) # Convert the model spec = xgb_converter.convert( xgb_model, self.feature_names, self.output_name, mode="classifier" ) if _is_macos() and _macos_version() >= (10, 13): # Get predictions df = pd.DataFrame(self.X, columns=self.feature_names) probabilities...
\ .setOutputCol("features") data_trans = vector_assembler.setHandleInvalid("keep").transform(data) xgb_classifier = SparkXGBClassifier(max_depth=5, missing=0.0, eval_metric='logloss', early_stopping_rounds=1, validation_indicator_col='isVal') xgb_clf_model = xgb_classifier.fit(data_trans)...