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。
# 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 >...
查看xgboost中XGBClassifier默认参数的方法 fromxgboost.sklearnimportXGBClassifierimportnumpyasnpx=np.array([[1,1,1],[1,1,0]])y=np.array([1,0])c=XGBClassifier()c.fit(x,y)print(c)输出为:XGBClassifier(base_score=0.5,booster='gbtree',colsample_bylevel=1,colsample_bynode=1,colsample_bytree=1...
Is it possible to pass a pyspark dataframe to a XGBClassifer as: from xgboost import XGBClassifer model1 = XGBClassifier() model1.fit (df.select(features), df.select('label')) If not, what is the best way to fit a pyspark dataframe to xgboost? Many thanks pyspark xgboost Share Impro...
I am passing a custom evaluation metric function to XGBClassifier fit(), but the Python API discards callable (i.e., custom) functions. Why? It also seems to discard any string inputs. Regardless of what I pass in, the results are the same. I tried all objective functions as well, and...
round() >>> xgb = XGBClassifier(n_estimators=10) >>> xgb = xgb.fit(X,y) >>> >>> get_xgb_imp(xgb,feat_names) {'var5': 0.0, 'var4': 0.20408163265306123, 'var1': 0.34693877551020408, 'var3': 0.22448979591836735, 'var2': 0.22448979591836735} 原文由 David 发布,翻译遵循 CC BY-SA...
L1正则化系数,默认为1 lambda L2正则化系数,默认为1 # 代码主要函数: 载入数据:load_digits() 数据拆分:train_test_split() 建立模型:XGBClassifier() 模型训练:fit() 模型预测:predict() 性能度量:accuracy_score() 特征重要性:plot_importance()
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 ...
模型训练:fit() 模型预测:predict() 性能度量:accuracy_score() 特征重要性:plot_importance() # -*- coding: utf-8 -*- """ ### # 作者:wanglei5205 # 邮箱:wanglei5205@126.com # 代码:http://github.com/wanglei5205 # 博客:http://cnblogs.com...
惩罚项系数,指定节点分裂所需的最小损失函数下降值。 调参: alpha L1正则化系数,默认为1 lambda L2正则化系数,默认为1 # 代码主要函数: 载入数据:load_digits() 数据拆分:train_test_split() 建立模型:XGBClassifier() 模型训练:fit() 模型预测:predict() ...