通过本篇博客记录一下Logistic regression的代码实现以及k-fold cross validation 的运用,数据集使用sklearn的breast cancer。 Logistic regression 与sklearn的实现有一些不同。 Gradient Stochastic gradient descent algorithm importnumpyasnpfromsklearn.datasetsimportload_breast_cancerfromsklearn.model_selectionimportKFol...
二、HoldOut Cross-validation(Train-Test Split) 三、K次交叉验证(K-Fold Cross-Validation) 四、分层K次交叉验证(Stratified K-Fold Cross-Validation) 五、Leave P Out cross-validation 六、Leave One Out cross-validation 七、蒙特卡罗交叉验证(Monte Carlo Cross-Validation) 八、时间序列交叉验证(Time Series...
二、HoldOut Cross-validation(Train-Test Split) 三、K次交叉验证(K-Fold Cross-Validation) 四、分层K次交叉验证(Stratified K-Fold Cross-Validation) 五、Leave P Out cross-validation 六、Leave One Out cross-validation 七、蒙特卡罗交叉验证(Monte Carlo Cross-Validation) 八、时间序列交叉验证(Time Series...
scores = cross_val_score(logreg,iris.data,iris.target,cv=loout)print("leave-one-out cross validation scores:{}".format(scores))print("Mean score of leave-one-out cross validation:{:.2f}".format(scores.mean())) 输出: Iris labels: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...
3.分层k折交叉验证/Stratified k-fold cross-validation 这与k 折交叉验证类似,但不同之处在于每次折叠保留整个数据集中类标签实例的百分比。因此,分层 k 折交叉验证对于类不平衡数据集效果很好。 我们可以使用 Scikit-learn StratifiedKFold()函数来执行分层 k 折交叉验证。折叠数在n_splits超参数中指定。
from sklearn.linear_model import LogisticRegression from sklearn.cross_validation import * from sklearn.metrics import * import 1. 2. 3. 4. 5. 6. 7. 8. 9. 我们产生 100 个数据,每个数据 10,000 个特征(我们需要产生一些输入和输出间纯粹偶然的联系)。
k-fold cross-validation ,其中,k一般取5或10。 标准交叉验证standard Cross validation demo: fromsklearn.model_selectionimportcross_val_score logreg=LogisticRegression()scores=cross_val_score(logreg,cancer.data,cancer.target)#cv:默认是3折交叉验证,可以修改cv=5,变成5折交叉验证。print("Cross validation ...
# 需要导入模块: from sklearn import cross_validation [as 别名]# 或者: from sklearn.cross_validation importcross_val_score[as 别名]deftest_cross_val_score_multilabel():X = np.array([[-3,4], [2,4], [3,3], [0,2], [-3,1], ...
1.1.2 sklearn中的交叉验证 fromsklearn.model_selectionimportcross_val_scorefromskelarn.datasetsimportload_irisfromskelarn.linear_modelimportLogisticRegression iris=load_iris()logreg=LogisticRegression()scores=cross_val_score(logreg,iris.data,iris.target)print("cross-validation scores: ",scores) ...
from sklearn.model_selectionimportcross_val_score logreg=LogisticRegression()scores=cross_val_score(logreg,cancer.data,cancer.target)#cv:默认是3折交叉验证,可以修改cv=5,变成5折交叉验证。print("Cross validation scores:{}".format(scores))print("Mean cross validation score:{:2f}".format(scores.mean...