Simple Ensemble Model using previous KNIME workflows
I posted making Random Forest, XGBoost, SVM Models previously.
So how about combining them all together. Here is what I did.
Top part is all learning part that can be copy and pasted from previous posts. I added save model in the end because I wanted to stop before going to the bottom part. Bottom part adds the probability of active/inactive given from each models and decides from addtion of three models using Math formula.
Very Simple and easy to make using KNIME again. Kaggle's ensemble models takes like hundreds of models so this is just a easy integration. But the things are getting bit complex here. In that case, using "Metanode" helps. Simply choose the nodes to group, right click, make them into metanodes. To get this!
Programming is all about input and output (IO) and this metanodes tidy up the space but also allows you to see the IO more easily. Another great option from KNIME!
Oh the result, Accuracy for Random Forest (0.71), XG (0.78), SVM(0.79) and Ensemble was 0.8. Wait, it's quite different from previous result... Partitioning maybe? But why is Random Forest so low...
