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