To get High accuracy by leaving low precision classes
Hi!!
I am using RapidMiner Studio 6.0.008.
To give some background I am trying to predict the posting Accounts for a given invoice using decision tree. (If you don't understand it doesn't really matter)
I used X-validation and as I can see from the PerformanceVector I got 87.06 % accuracy. I am ok with the model and I want to go use it.
However, since the application is very sensitive I want to use the cases where I have 100% class precision.
What I want is when I give my model a test example(unseen example) the model should :
- predict only when it is 95% sure (to make it more general say above some threshold )
I am using RapidMiner Studio 6.0.008.
To give some background I am trying to predict the posting Accounts for a given invoice using decision tree. (If you don't understand it doesn't really matter)
I used X-validation and as I can see from the PerformanceVector I got 87.06 % accuracy. I am ok with the model and I want to go use it.
However, since the application is very sensitive I want to use the cases where I have 100% class precision.
What I want is when I give my model a test example(unseen example) the model should :
- predict only when it is 95% sure (to make it more general say above some threshold )
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I found the operator Drop Uncertain Predictions.