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When users apply cross-validation to select a good configuration , then they might want to balance the cross-validated choice with their own estimate of the configuration. In this way, they can attempt to counter the volatility of cross-validation when the sample size is small and include relevant information from previous research.
In statistics, the jackknife (jackknife cross-validation) is a cross-validation technique and, therefore, a form of resampling. It is especially useful for bias and variance estimation. The jackknife pre-dates other common resampling methods such as the bootstrap.
Cross-validation is employed repeatedly in building decision trees. One form of cross-validation leaves out a single observation at a time; this is similar to the jackknife. Another, K-fold cross-validation, splits the data into K subsets; each is held out in turn as the validation set. This avoids "self-influence".
cross-validation, in which the parameters (e.g., regression weights, factor loadings) that are estimated in one subsample are applied to another subsample. Bootstrap aggregating (bagging) is a meta-algorithm based on averaging model predictions obtained from models trained on multiple bootstrap samples.
Out-of-bag (OOB) error, also called out-of-bag estimate, ... Cross-validation (statistics) Random forest; Random subspace method (attribute bagging) References
This can be done by cross-validation, or by using an analytic estimate of the shrinkage intensity. The resulting regularized estimator ( δ A + ( 1 − δ ) B {\displaystyle \delta A+(1-\delta )B} ) can be shown to outperform the maximum likelihood estimator for small samples.
Instead of fitting only one model on all data, leave-one-out cross-validation is used to fit N models (on N observations) where for each model one data point is left out from the training set. The out-of-sample predicted value is calculated for the omitted observation in each case, and the PRESS statistic is calculated as the sum of the squares ...
The KL can be estimated using a cross-validation method, although KL cross-validation selectors can be sub-optimal even if it remains consistent for bounded density functions. [17] MH selectors have been briefly examined in the literature. [18]