Random forest performs worse than single decision tree

[mlauber71]

@jeandony

In the validation set I observe the same phenomenon: AUC of random forest around 0.50 and decision tree around 0.78-0.79.

This sounds pretty strange. One guess could be that you have some restrictions in the Random forests or some overfitting in the Decision Tree. You should check if you do really use the same data files in both cases.

Maybe you could try and see if you could benchmark your case with this AutoML workflow:

H2O.ai AutoML in KNIME for classification problems

H2O.ai Automl - a powerful auto-machine-learning framework wrapped with KNIME It features various models like Random Forest or XGBoost along with Deep Learning. It has wrappers for R and Python but also could be used from KNIME. The results will be written to a folder and the models will be stored in MOJO format to be used in KNIME (as well as on a Big Data cluster via Sparkling Water). One major parameter to set is the running time the model has to test various models and do some hyperparam…

You could force H2O to just consider Tree algorithms and see what the result is.