@Jitudeepu99 welcome to the KNIME forum. First I would like to point you to this entry about KNIME and machine learning in general, also about using Python nodes :
@Everfresh000 welcome to the KNIME forum. As a basic remark: since this is an exam the topics should be covered in the course. But anyway…
If you want to start about learning KNIME and machine-learning I would recommend to take these two courses on the KNIME Learning platform https://knime.learnupon.com/ .
[L1-DS] KNIME Analytics Platform for Data Scientists: Basics
[L2-DS] KNIME Analytics Platform for Data Scientists: Advanced
(Examples and workflows for courses are provided here: knime/Educ…
Then ether are the scorer nodes that would be able to give back some statistics you might need - Scorer , Numeric Scorer - this one has MAPE , H2O Numeric Scorer , Scorer Java Script and for Binary Class Problems there is the: Binary Classification Inspector .
For some models LogLoss might also be an option.
@akhtarameen the Decision tree of @aworker does a wonderful job. I wanted to see what else is possible so I took it up a notch. H2O.ai and XGBoost are able to do an even slighly better job with more correct classification (on the test set).
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Admitteldly H2O.ai would add a further level of complication since it would use an ensemble of models to predict the outcome. If you data would differ over time very complicated models might also be less robust than a tree model. But from my expe…
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