n_estimators:提升树的数量,即训练轮数,等价于原生库的num_boost_round max_depth:树的最大深度 learning_rate:学习率,等价于原生库的eta verbosity:控制学习过程中输出信息的多少,取值为0, 1, 2, 3 objective:学习目标及其损失函数,默认为reg:squarederror,即以平方损失为损失函数的回归模型 booster:弱评估器,...
cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], folds =cv_folds, metrics='mlogloss', early_stopping_rounds=early_stopping_rounds) n_estimators = cvresult.shape[0] alg.set_params(n_estimators = n_estimators) print (cvresult) #result = pd.DataF...
# 需要导入模块: from sklearn.ensemble import AdaBoostClassifier [as 别名]# 或者: from sklearn.ensemble.AdaBoostClassifier importn_estimators[as 别名]defevaluate(category, clf, datamanager, data=(None, None)):"""Run evaluation of a classifier, for one category. If data isn't set explicitly...
[0:num_preds].compute()) # non-dask np.random.seed(1234) random.seed(1234) dtrain = xgb.DMatrix(X, y) output = xgb.train(params, dtrain, num_boost_round=params['n_estimators']) preds = output.predict(dtrain) print(preds[0:num_preds]) model = xgb.XGBRegressor(**params) model....
xgb_param['num_class'] = 9 xgtrain = xgb.DMatrix(X_train, label = y_train) cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], folds =cv_folds, metrics='mlogloss', early_stopping_rounds=early_stopping_rounds) ...
xgb_param['num_class'] = 9 xgtrain = xgb.DMatrix(X_train, label = y_train) cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], folds =cv_folds, metrics='mlogloss', early_stopping_rounds=early_stopping_rounds) ...
Gradient boosting fits the tree until it reaches the extreme number of estimators. The sum of all scaled output from the tree in the ensemble and the initial guess predict the output for the new input. The HGB with a derivable loss function is adaptable to any regression or classification ...