Calculate a trace of cross-validation error rate for SODA forward-backward procedureYang LiJun S. Liu
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...
F平方误差(squared error):D(y,y^)=(y−y^)2 分类误差(classification error):D(y,y^)={1,y≠y^0,y=y^ 为了估计误差,假设训练集d中的点对(xi,yi)由Rp+1的某概率分布F随机抽样得到,即 (3)(xi,yi)∼iidF,i=1,⋯,N. 训练规则rd(x)的真实误差率(true error rate)Errd为与d独立的新...
我们首先有不同复杂度的modle,然后利用training data进行训练,利用validation set验证,Error求和,选择error最小的,最后选择模型输出,计算Final Error!
= "blue", main = "ROC Curve", xlab = "False Positive Rate", ylab = "True Positive Rate"...
However, segmented cross-validation has the advantage of being able to exploit structures in the data to ascertain error rates. For example, if the data contain replicates, the error rate computed by a full cross-validation of the data will give overoptimistic results since the same samples are...
And what you suggest, is to sample over this out-of-fold prediction to calculate with error rate. 6、I don't wanted to state, that single hold-out sets are ano-go, they have their applications. Forecasting problems are probably the best example for that. In competitions like that (e. ...
p pBackground/p pCross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed metho... S Varma,R Simon - 《Bmc Bioinformatics》 被引量: 804发表: 2006年 Estimating classification error rate: Repeated cross-validation, repea...
However, if the overall goal is to find the classification rule with the smallest error rate, this depends only on the conditional density p(y|x). Discriminative methods directly model the conditional distribution, without assuming anything about the input distribution p(x). Well known generative-...
names(res) <- "error rate" return(res) } # specify `eval_metric` argument for measuring the error rate # instead of the (default) accuracy crossvalidation::crossval_ml(x = X, y = y, k = 5, repeats = 3, fit_func = randomForest::randomForest, ...