Kim, J.-H., Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap. Comput. Stat. Data Anal. 53:3735–3745, 2009.Kim, J.-H.: Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap. Computational Statistics and...
假阳性率（False Positive Rate, FPR）：真实类别为负例的样本中，被错误分类为正例的比例。ROC曲线：...
Since this cross-validation error is just an average, the standard error of that average also gives us a standard error of the cross-validation estimate: We take the error rates from each of the folds. Their average is the cross-validation error rate. The standard error is the standard...
若干轮(小于S)之后,选择损失函数评估最优的模型和参数。 第三种是留一交叉验证(Leave-one-out Cross Validation),它是第二种情况的特例,此时S等于样本数N,这样对于N个样本,每次选择N-1个样本来训练数据,留一个样本来验证模型预测的好坏。此方法主要用于样本量非常少的情况,比如对于普通适中问题,N小于50时,我一...
训练规则rd(x)的真实误差率(true error rate)Errd为与d独立的新点对(x0,y0)中y0的预测y^0=rd(x0)与y0的差异的期望 (4)Errd=EF{D(y0,y^0)}. 当F未知时,可以借助于经验分布。 12.1 交叉验证 对于上述Err=EF{D(y0,y^0)},第一种直观的猜想是其为训练集中各yi的预期预测值的平均差异,被称为表...
We construct a prediction rule on the basis of some data, and then wish to estimate the error rate of this rule in classifying future observations. Cross-validation provides a nearly unbiased estimate, using only the original data. Cross-validation turns out to be related closely to the bootst...
R语言模拟:Cross Validation 前两篇在理论推导和模拟的基础上,对于误差分析中的偏差方差进行了分析。本文在前文的基础上,分析一种常用的估计预测误差进而可以参数优化的方法:交叉验证,并通过R语言进行模拟。 K-FOLD CV 交叉验证是数据建模中一种常用方法,通过交叉验证估计预测误差并有效避免过拟合现象。简要说明CV(...
k折交叉验证(k-fold cross validation):首先随机地将已给数据切分为k个互不相交的大小相同的子集,然后利用k-1个子集的数据训练模型,利用余下的子集测试数据,将这一过程对可能的k中选择重复进行,最后选出k次评测中平均测试误差最小的模型。 总结:实际使用时,我们通过训练集学习到参数,再计算交叉验证集上的error,...
returns a 10-fold cross-validation error estimate for the functionpredfunbased on the specifiedcriterion, either'mse'(mean squared error) or'mcr'(misclassification rate). The rows ofXandycorrespond to observations, and the columns ofXcorrespond to predictor variables. ...
trainAccuracy = 1-trainError trainAccuracy = 0.9431 Typically, the misclassification error on the training data is not a good estimate of how a model will perform on new data because it can underestimate the misclassification rate on new data. A better estimate is the cross-validation error. ...