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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.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
The amount of overfitting can be tested using cross-validation methods, that split the sample into simulated training samples and testing samples. The model is then trained on a training sample and evaluated on the testing sample.
In evaluating the basics of data validation, generalizations can be made regarding the different kinds of validation according to their scope, complexity, and purpose. For example: Data type validation; Range and constraint validation; Code and cross-reference validation; Structured validation; and; Consistency validation
Walk Forward Analysis is now widely considered the "gold standard" in trading strategy validation. The trading strategy is optimized with in-sample data for a time window in a data series. The remaining data is reserved for out of sample testing. A small portion of the reserved data following the in-sample data is tested and the results are ...
When bootstrap aggregating is performed, two independent sets are created. One set, the bootstrap sample, is the data chosen to be "in-the-bag" by sampling with replacement.
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 method of model validation that iteratively refits the model, each time leaving out just a small sample and comparing whether the samples left out are predicted by the model: there are many kinds of cross validation. Predictive simulation is used to compare simulated data to actual data.