High Accuracy, low recall and low precision - how to optimise this?

lordlord Member Posts: 1 Newbie
Hi experts,

I have a dataset with about 40,000 data and would like to do a classification. I have a binominal label (yes/no). To create the model I take a decision tree. Then I want to apply the created model to a training data set (30,000 data) via the operator Apply model.

Overall I have a very high accuracy, of almost 94%. But my problem is that the class "no" has a very high recall (98%) and a high precision (94%). The class "yes", on the other hand, has a recall of 7% and a precision of 19%.

I work with the Optimize operator (Grid). I also use Cross Validation as a sub-process. Furthermore I work with the Performance Operator (Classification) and I have already used accuracy and kappa as main criteria.

I know that there have already been similar questions here in the community, but unfortunately they haven't helped me yet.

Really looking forward to your help & thanks already upfront!


  • MartinLiebigMartinLiebig Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,421 RM Data Scientist
    first I would consider to move away from a Decision Tree and try a Random Forest. Your Decision Tree is likely a small one, which mostly predicts " yes" and only in rare cases predicts "no". You are bias towards the majority class of your sample.

    Afterwards you may consider to tune your threshold using the respective threshold operators.

    - Sr. Director Data Solutions, Altair RapidMiner -
    Dortmund, Germany
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