This workflow shows how the random forest nodes can be used for classification and regression tasks. It also shows how the "Out-of-bag" data that each random forest learner calculates can be used to estimate the accuracy of a random forest.
This workflow shows how the prediction fusion node can be used to combine the predictions of a naive bayes and a svm classifier.
The challenge is to blend together models from different analytics platforms - i.e. Python , R, and KNIME - to create an ensemble model. Data is the “airline data set” (http://stat-computing.org/dataexpo/2009/the-data.html) enriched with additional external data , such as cities, daily weather (https://www.ncdc.noaa.gov/cdo-web/datasets/), US holidays, geo-coordinates, airplane maintenance. DepDealys is used as the target variable.
This workflow shows how the tree ensemble nodes can be used for regression and classification tasks. Note: If you want to deploy a random forest, we recommend to use the less complex random forest nodes.