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The Five Fold CV block helps you determine whether you have chosen the best
neural network configuration for your problem. You can use it to test a data set
against different types of neural networks or different configurations of the same
type of neural network. It splits the data set into five subsets and then trains and
tests your network a total of five times, each time using a different one of the five
subsets for training.
Note: The Train and Test block is an encapsulation block that contains a
NeurOn-Line diagram on its subworkspace.
Attach the Five Fold CV block's top action link to your data set and the bottom
action link to your neural network. You cannot attach more than one neural
network or data set to the block.
configure from its menu. It displays
two dialogs, one on top of the other. They are for the Training and Fit Tester blocks
that are on the Five Fold CV block's subworkspace. For more information on how
to configure them, see "Trainer" and "Fit Tester".
To evaluate the Five Fold CV block, you must pass it a control signal. The block
randomly divides the data set into five subsets of equal size. It trains and tests
your neural network five times. The first time, it trains the network with the first
subset and tests it with the other four. The second time, it trains the network with
the second subset and tests it with the other four. The block continues until its
trained and tested the block five times, each time using a different fifth of the data
set for training.
In each case, the training error is an indicator of how well the network fits the
training data. The error will generally decrease if you expand the network architecture, even when the network is overfitting. Therefore, do not use the training
error to select the optimum network architecture.
Configuring
When you choose configure from the block's menu, it displays two dialogs, one
for configuring the Training block and the other for configuring the Fit Tester
block, both of which are on the Five Fold CV block's subworkspace.
For information on configuring the Fit Tester block, see "Configuring":
Example
This example uses Five Fold CV blocks to see which of three neural network
configurations is the best. All networks use exactly the same data set. To
determine which configuration fits the data best, look at the number passed from
the bottom port of the Five Fold CV block. The lower the number, the better the fit
for data not seen in the training process.
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See Also
For more information on how to use this block, see the pages below.
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Basic Block Behavior
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"Neural Network Blocks" Chapter
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Data Set
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Trainer
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Fit Tester
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Train and Test
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Copyright © 1996, Gensym Corporation, Inc.