Penalizing misclasslification from imbalanced data

[ScottF]

The option you’re looking for in the XGBoost Tree Ensemble Learner is on the Booster tab - it’s called “Scale positive weights”. From the the node description:

Scale positive weight

Controls the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative instances) / sum(positive instances).

Note that with this approach you have to be careful to set your positive class appropriately as described here:

xgboost: Scale positive weight

For unbalanced data in binary classification this is very important. The logic in xgboost is that class value of 0 = negative class and class value of 1 = positive class. In KNIME however it’s not possible to use a integer as class value for xgboost and there is no setting to define which is the positive class. It’s completely obscure which class is assigned the positive class. In fact I have string column with 0/1 as classes and 0 seems to be labeled as the positive class. My suggestion is to…

Check here for another possible alternative using the H2O nodes:

XGBoost regression with poor accuracy

Hi @zizoo, the XGBoost nodes in KNIME allow to use weights in the binary classification setup. See discussion about a caveat here: xgboost: Scale positive weight. This will not directly apply to your original regression problem, but once you re-formulate in a binary classification context. Alternatively, you can use https://hub.knime.com/knime/extensions/org.knime.features.ext.h2o/latest/org.knime.ext.h2o.nodes.learner.drf.regression.H2ODRFRegressionLearnerNodeFactory3 that allows to use a c…