How do I introduce random/new data into a Neural Net model?

Question

I have built a (supervised training) Neural Net model to predict instances of Diabetes.  How do I introduce new patient data to the model to get predictions on these patients?

MartinLiebig · Accepted Answer

Hi,what you did was using a Feature Selection, whose task it is to give you the best possible columns to build a model on, by using a model. You would need to use another ANN on this and then the Apply Model operator to score unknown data.
Have you seen the academy? academy.rapidminer.com should help you a lot.

Best,Martin

CKönig · Accepted Answer

Hello @FrancoisR, in this case, you would need to rebuild the model on the whole dataset (old and new combined) to create a better model. This is the usual way to handle new data. This also helps you to discard possibly outdated information from your training dataset, depending on your actual use case. However, if the algorithm for creating a machine learning model supports it, we do have the option to update pre-trained models with new data. This capability can be checked in the Operator Information: An example for an updatable model would be a Naive Bayes model. Updating an existing model trained somewhen earlier can be done by the Update Model (surprise!) operator: See this example process (copy the whole xml code, click in the background of an empty process and press "Ctrl+V" to paste it there):

Old data

create a model based on "old data"

New data

apply old model on new, unseen data

update the model with new data

apply the updated model on the new data

Increased performance on new dataset

Kind regards, Christian