开发者ID:jonathanwoodard,项目名称:scikit-learn,代码行数:28,代码来源:test_cross_validation.py 示例11: cross_validate ▲点赞 1▼ defcross_validate(self, k=10):"""Performs a k-fold cross validation of our training data. Args: k: The number of folds for cross validation. """self.scores =...
scores = cross_validate(clf, iris.data, iris.target, scoring=scoring, cv=5, return_train_score=False) print(scores.keys()) # 显示dict的key print(scores['test_recall_macro']) # 打印其中一个方法的值 ''' 结果: dict_keys(['fit_time', 'score_time', 'test_precision_macro', 'test_rec...
在上述代码中,首先使用read.csv函数读取数据,并将特征变量和目标变量分别存储在x和y中。然后,使用trainControl函数定义交叉验证控制参数,其中method参数指定为"cv"表示使用交叉验证方法,number参数指定为5表示进行5折交叉验证。接下来,使用train函数训练模型,并通过$results属性获取CV错误和训练错误。最后,使用print函数打...
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Cross validate results, this will print result out as [iteration] metric_name:mean_value :param xgb.core.Booster model: :param Tuple[xgb.DMatrix, xgb.DMatrix] data: MNIST database train and test data and labels :param argparse.Namespace args: An object to take the attributes ...
score_ndarray = cross_val_score(gbr_model, X, y, cv=kf)print(score_ndarray) score_ndarray.mean() 上面一段代码的运行结果如下图所示: image.png 从上面的运行结果可以看出,集成回归模型取得了较好的回归效果。 5.选择最优模型 使用sklearn.model_selection库中的cross_validate方法,需要传入4个参数,第1...
df.loc[row_index,'Parameters']=str(estimator.get_params())cv_results=cross_validate(estimator,X,y,cv=cv_split)df.loc[row_index,'Train Accuracy Mean']=cv_results['train_score'].mean()df.loc[row_index,'Test Accuracy Mean']=cv_results['test_score'].mean()df.loc[row_index,'Test Accu...
使用sklearn.model_selection库中的cross_validate方法,需要传入4个参数,第1个参数为模型对象estimator,第2个参数为特征矩阵X,第3个参数为预测目标值y,第4个关键字参数cv数据类型为交叉验证对象,函数返回结果的数据类型为字典。 如果你的sklearn版本小于0.19,则无法调用cross_validate方法。
27, Article 10 advantage of using surveys conducted at the same point of time from various sources is that by comparing the results one can cross-validate the quality of the data from these sources (=-=Beckers 2010-=-). By implementing these two types of survey data in the analysis the ...
Amend single display class to optionally return list (ENH addfrom_cv_resultsinRocCurveDisplay(singleRocCurveDisplay)#30399) For all 3 options we take the output ofcross_validate, and use the fitted estimator and test indicies. No fitting is done in the display. ...