Cross validation

Cross validation
One of the classical model "re-sampling" validation techniques.

Cross validation has some obvious advantages. If training a single network, we would probably reserve 25% of the data for test. By using cross validation, we can reduce the individual test set size.

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Cross validation
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Figure 8: Model evaluation using cross validation.
The classifier checks values of all attributes for input records and compares those values
with the values predicted by the preprocess filter using its algorithm. After the ...

CART accomplishes this by building a very complex tree and then pruning it back to the optimally general tree based on the results of cross validation or test set validation.

We'll review testset validation, leave-one-one cross validation (LOOCV) and k-fold cross-validation, and we'll discuss a wide variety of places that these techniques can be used. We'll also discuss overfitting...

* "Selecting Neural Network Architectures via the Prediction Risk: Application to Corporate Bond Rating Prediction" by Joachim Utans and John Moody available from the Ohio State Neuroprose archive. The authors use backprop, v-fold cross validation ...

"Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation"
"Efficient Algorithms for Minimizing Cross Validation Error"
"Note on free lunches and cross-validation" ...

Ten individual groups were created such that all of the Test Sets were mutually exclusive. This type of analysis is know as 10-fold cross validation, and is a very useful method to validate classifiers of this type.

See also: Validation, Classification, Regression, Distribution, Neural network