misclassification costs to adjust for rare outcome

[mlauber71]

@BruceJohnson for further ideas you could check out my collection about machine leaning. Especially the entries marked unbalanced.

Machine Learning Meta Collection (with KNIME) – mlauber71

This thread also was about unbalanced data with multi class but might still give some ideas.

Large Unbalanced Data

@User10000 welcome to the KNIME forum. If I understand correctly you have a large dataset with a multiclass task (15? possible targets). So one class you do not want (number 16?). This might be tough if not impossible, I fear. Things you could still try: combine some of the targets so the classes get reduced work with missclassification costs (you will have to try a few things. Maybe try to configure this weka node). Check this article though it does not cover imbalanced multi class Try to w…

One approach could be. Use H2O auto machine learning and only xgboost (or GBM if you are on a Windows machine) with AUCPR as a metric.

Also you might check your data if there are special anomalies or you can further prepare the data. In the collection there are also links about that but we might need to know more about the data.