Decision tree learner seems to be overfitting

[mlauber71]

@akhtarameen concerning overfitting you might want to read about the evaluation of predictive models. In this case the question might be where to cut-off the 0/1 decision. The default of 0.5 might not be the right one.

Machine Learning Meta Collection (with KNIME) – mlauber71

More also about finding the right cut-off point here:

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…