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# model problem: finding the 'good' observation in each set of observations

Hello,

I'm somewhat new to rapidminer and need help / suggestions on how to setup a process for the following problem:

I have a pretty lage dataset (over 1 mio observations): there are 1-20 observations with the same key. Besides that they have 4 more numeric attributes.

Also there is one label (0 or 1) and it's 1 for just one observation in each 'key-group'.

In other words: for each key there is exactly one observation marked with a 1 and all other have a 0.

The challenge is to find that correlation between the other 4 attributes and the label.

If I just use a decision tree, then obviously it doesn't understand that all observations with the same key belong together.

Any advice would be really great - especially how I model that there is just one 'good' observation for each key.

Thank you

Tobias

I'm somewhat new to rapidminer and need help / suggestions on how to setup a process for the following problem:

I have a pretty lage dataset (over 1 mio observations): there are 1-20 observations with the same key. Besides that they have 4 more numeric attributes.

Also there is one label (0 or 1) and it's 1 for just one observation in each 'key-group'.

In other words: for each key there is exactly one observation marked with a 1 and all other have a 0.

The challenge is to find that correlation between the other 4 attributes and the label.

If I just use a decision tree, then obviously it doesn't understand that all observations with the same key belong together.

Any advice would be really great - especially how I model that there is just one 'good' observation for each key.

Thank you

Tobias

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