CrossValidationDriver(examples,
attrs=[],
nPossibleVals=[],
holdOutFrac=.3,
silent=0,
tolerance=0.3,
calcTotalError=0,
hiddenSizes=None,
**kwargs)
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**Arguments**
- examples: the full set of examples
- attrs: a list of attributes to consider in the tree building
*This argument is ignored*
- nPossibleVals: a list of the number of possible values each variable can adopt
*This argument is ignored*
- holdOutFrac: the fraction of the data which should be reserved for the hold-out set
(used to calculate the error)
- silent: a toggle used to control how much visual noise this makes as it goes.
- tolerance: the tolerance for convergence of the net
- calcTotalError: if this is true the entire data set is used to calculate
accuracy of the net
- hiddenSizes: a list containing the size(s) of the hidden layers in the network.
if _hiddenSizes_ is None, one hidden layer containing the same number of nodes
as the input layer will be used
**Returns**
a 2-tuple containing:
1) the net
2) the cross-validation error of the net
**Note**
At the moment, this is specific to nets with only one output
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