13_Meta_Learning
This directory contains 5 workflows.
01_Combining_Classifiers_using_Prediction_Fusion
This workflow shows how the prediction fusion node can be used to combine the predictions of a naive bayes and a svm classifier.
02_Learning_a_Random_Forest
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 […]
03_Learning_a_Tree_Ensemble_Model
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 […]
04_Cross-Platform_Ensemble_Model
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 […]
05_Weak_Supervision_on_the_Adult_dataset
This workflow shows how to use the Weak Label Model Learner and Predictor nodes to aggregate sources of weak supervision such as weak models or simple rules […]