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In cross-validation, Training Server follows these steps:

  1. It builds one model using all of the data.
  2. It divides the data into x partitions, where x = 3, 5, or 10.
  3. It builds a number of partial models: as many as there are partitions, each one using a different combination of x -1 partitions.
    For example, if the data is divided into the three partitions A, B, and C, Training Server builds model X using partitions A and B, model Y using partitions A and C, and model Z using partitions B and C.
  4. It tests each of these partial models against the partition that it omitted when it was built.
    In the example, it tests model X against partition C, model Y against partition B, and model Z against partition A.
  5. It aggregates the results of all these tests and presents them as the rating of the entire model.

These ideas underlie the concept of cross-validation:

  • The best way to test a model is to apply it to data that was not used in building the model.
  • A model built using most of the data is usefully similar to the model built using all of the data, so the results of testing (for example) all possible 90-percent models are a good indication of the quality of the 100-percent model.

Because cross-validation adds to the time required to build a model, you may not want to select cross-validation for very large training objects or for objects for which you selected training quality Regular Level 6.

This page was last modified on December 11, 2018, at 11:51.


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