Faq choose between different cross validation leave out options: Difference between revisions

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For details on the different methods, see our Wiki page: [[Using Cross-Validation]]
For details on the different methods, see our Wiki page: [[Using Cross-Validation]]
and, in particular, [[Using Cross-Validation | Choosing the Cross-Validation Method]]
and, in particular, [[Using Cross-Validation#Choosing the Cross-Validation Method | Choosing the Cross-Validation Method]]
 
 
'''Still having problems? Please contact our helpdesk at [mailto:helpdesk@eigenvector.com helpdesk@eigenvector.com]'''


[[Category:FAQ]]
[[Category:FAQ]]

Latest revision as of 13:15, 8 January 2019

Issue:

How do I choose between the different cross-validation leave-out options?

Possible Solutions:

There are many attributes that influence selection of the appropriate cross-validation scheme for a given situation:

  • The ordering of the samples in the dataset,
  • The total number of objects (and variables) in the dataset
  • The presence (or lack thereof) of replicate samples in the dataset
  • The specific objective(s) of the analysis,
  • The consequences/costs of overly optimistic or overly pessimistic results, and
  • The amount of time available to do cross-validation

For details on the different methods, see our Wiki page: Using Cross-Validation and, in particular, Choosing the Cross-Validation Method


Still having problems? Please contact our helpdesk at helpdesk@eigenvector.com