The Altair Community is migrating to a new platform to provide a better experience for you. In preparation for the migration, the Altair Community is on read-only mode from October 28 - November 6, 2024. Technical support via cases will continue to work as is. For any urgent requests from Students/Faculty members, please submit the form linked here
How to Generate Optimum ExampleSet
giannidiorio
Member Posts: 2 
Contributor I
Hello to everyone,
I'm quite new in RapidMiner and I had a little problem or better question: How can I generate optimum ExampleSet?
I try to explain myself:
I want to implement an optimization process in RM5.3, basically my objective is to create a process where I have a classification algorithm trained with a set of configurations (i.e. the training data is constituted by a set of attribute each one representing a configuration parameter, the label is simply 1 for good configuration and 0 for bad configuration). Once the algorithm is trained and the model is created my question is: how to start a cycle to find a good exampleSet to feed the ApplyModel operator (i.e. an exampleSet that is able to give 1 when processed)? I have created also a correlation matrix operator in order to identify the attributes of the training data with higher weight, however I'm not able to loop and change this attributes until the result is 1. How can I implement it? I want to create an auto-adaptation process in java, or in other words I have a set of good and bad configurations and I want to start a process which result is a good configuration.
Thank you for the help.
Best Regards,
Giovanni
I'm quite new in RapidMiner and I had a little problem or better question: How can I generate optimum ExampleSet?
I try to explain myself:
I want to implement an optimization process in RM5.3, basically my objective is to create a process where I have a classification algorithm trained with a set of configurations (i.e. the training data is constituted by a set of attribute each one representing a configuration parameter, the label is simply 1 for good configuration and 0 for bad configuration). Once the algorithm is trained and the model is created my question is: how to start a cycle to find a good exampleSet to feed the ApplyModel operator (i.e. an exampleSet that is able to give 1 when processed)? I have created also a correlation matrix operator in order to identify the attributes of the training data with higher weight, however I'm not able to loop and change this attributes until the result is 1. How can I implement it? I want to create an auto-adaptation process in java, or in other words I have a set of good and bad configurations and I want to start a process which result is a good configuration.
Thank you for the help.
Best Regards,
Giovanni
0
