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  2. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    The validation data set functions as a hybrid: it is training data used for testing, but neither as part of the low-level training nor as part of the final testing. The basic process of using a validation data set for model selection (as part of training data set, validation data set, and test data set) is: [10] [14]

  3. Data validation - Wikipedia

    en.wikipedia.org/wiki/Data_validation

    Data type validation is customarily carried out on one or more simple data fields. The simplest kind of data type validation verifies that the individual characters provided through user input are consistent with the expected characters of one or more known primitive data types as defined in a programming language or data storage and retrieval ...

  4. Data analysis - Wikipedia

    en.wikipedia.org/wiki/Data_analysis

    Cross-validation. By splitting the data into multiple parts, we can check if an analysis (like a fitted model) based on one part of the data generalizes to another part of the data as well. [144] Cross-validation is generally inappropriate, though, if there are correlations within the data, e.g. with panel data. [145]

  5. Data validation and reconciliation - Wikipedia

    en.wikipedia.org/wiki/Data_validation_and...

    Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.

  6. Data verification - Wikipedia

    en.wikipedia.org/wiki/Data_verification

    Data verification helps to determine whether data was accurately translated when data is transferred from one source to another, is complete, and supports processes in the new system. During verification, there may be a need for a parallel run of both systems to identify areas of disparity and forestall erroneous data loss .

  7. Cross-validation (statistics) - Wikipedia

    en.wikipedia.org/wiki/Cross-validation_(statistics)

    This method, also known as Monte Carlo cross-validation, [21] [22] creates multiple random splits of the dataset into training and validation data. [23] For each such split, the model is fit to the training data, and predictive accuracy is assessed using the validation data. The results are then averaged over the splits.

  8. Data processing - Wikipedia

    en.wikipedia.org/wiki/Data_processing

    Data processing may involve various processes, including: Validation – Ensuring that supplied data is correct and relevant. Sorting – "arranging items in some sequence and/or in different sets." Summarization (statistical) or – reducing detailed data to its main points. Aggregation – combining multiple pieces of data.

  9. Multiple comparisons problem - Wikipedia

    en.wikipedia.org/wiki/Multiple_comparisons_problem

    Multiple comparisons arise when a statistical analysis involves multiple simultaneous statistical tests, each of which has a potential to produce a "discovery". A stated confidence level generally applies only to each test considered individually, but often it is desirable to have a confidence level for the whole family of simultaneous tests. [ 4 ]