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If cross-validation is used to decide which features to use, an inner cross-validation to carry out the feature selection on every training set must be performed. [30] Performing mean-centering, rescaling, dimensionality reduction, outlier removal or any other data-dependent preprocessing using the entire data set.
In analytical chemistry, cross-validation is an approach by which the sets of scientific data generated using two or more methods are critically assessed. [1] The cross-validation can be categorized as either method validation [ 1 ] or analytical data validation.
Cross-validation may refer to: Cross-validation (statistics) , a technique for estimating the performance of a predictive model Cross-validation (analytical chemistry) , the practice of confirming an experimental finding by repeating the experiment using an independent assay technique
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 .
To confirm the model's performance, an additional test data set held out from cross-validation is normally used. It is possible to use cross-validation on training and validation sets, and within each training set have further cross-validation for a test set for hyperparameter tuning. This is known as nested cross-validation.
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 ...
Cross-validation is a statistical method for validating a predictive model. Subsets of the data are held out for use as validating sets; a model is fit to the remaining data (a training set) and used to predict for the validation set. Averaging the quality of the predictions across the validation sets yields an overall measure of prediction ...
Cross-validation is an alternative that is applicable to non time-series scenarios. Cross-validation involves splitting multiple partitions of the data into training set and validation set – instead of a single partition into a training set and validation set.