Altair RISE

A program to recognize and reward our most engaged community members

Nominate Yourself Now!

"Early stopping based on cross-validation"

Dear all,
I am using RapidMiner Studio 5.3 and I would like use the early stopping based on cross-validation (see http://en.wikipedia.org/wiki/Early_stopping#Early_stopping_based_on_cross-validation) in the training of neural networks. I don't find in RapidMiner any operator which could help me..Is this feature implemented in some way? Or do the developers plan to implement it?

Thank you!

ilaria

Find more posts tagged with

AI Studio

Cross Validation

Accepted answers

All comments

fras

The current implementation of the NeuralNet-Operator uses the
parameter epsilon as stopping criterium. No matter which criteria you
apply you always want to avoid overfitting your data. Finally, the
ultimative way to check whether your model overfits your data is using
the operator X-Validation together with a Performance-Operator. It
offers the possibility to check other algorithms also that may more
appropriate for you data.

MariusHelf

Let me add that you can optimize the parameters of the Neural Net operator: while it is not possible to perform any kind of early stopping, you can try different configurations for the Neural Net, validate them with the X-Validation operator, and at the end select those parameters that worked best.

To help you to automate this you can use the Optimize Parameters operator.

Best regards,
Marius