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03_​Learning_​a_​Tree_​Ensemble_​Model

How to use the Tree Ensemble nodes

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.

How to use the Tree Ensemble nodes for classification and regression This workflow shows how the Tree Ensemble nodes can be used for classification andregression tasks. Note:If you would only like to train a random forest,please check out our less complex RandomForest nodes read workflow local datapartition data in 80% training20% testingusing stratified samplingscore the predictiononly keeppetal width andPrediction(petal width)draw a line plotto visualize the performanceof the simple regression treescore the classificationpredictiontrain an ensemble of 100 trees of which each usesa random column sample with 50% of the columns anda random row sample with 50% of the rows for training.(In this case we use petal width as target column)predict the targetcolumn of the test datatrain an ensemble of 100 trees of which each usesa random column sample with 50% of the columns anda random row sample with 50% of the rows for training.The split criterion is Information Gain Ratiopredict the classof the test data File Reader Partitioning Numeric Scorer(deprecated) Column Filter Line Plot (local) Scorer Tree Ensemble Learner(Regression) Tree Ensemble Predictor(Regression) Tree EnsembleLearner Tree EnsemblePredictor How to use the Tree Ensemble nodes for classification and regression This workflow shows how the Tree Ensemble nodes can be used for classification andregression tasks. Note:If you would only like to train a random forest,please check out our less complex RandomForest nodes read workflow local datapartition data in 80% training20% testingusing stratified samplingscore the predictiononly keeppetal width andPrediction(petal width)draw a line plotto visualize the performanceof the simple regression treescore the classificationpredictiontrain an ensemble of 100 trees of which each usesa random column sample with 50% of the columns anda random row sample with 50% of the rows for training.(In this case we use petal width as target column)predict the targetcolumn of the test datatrain an ensemble of 100 trees of which each usesa random column sample with 50% of the columns anda random row sample with 50% of the rows for training.The split criterion is Information Gain Ratiopredict the classof the test dataFile Reader Partitioning Numeric Scorer(deprecated) Column Filter Line Plot (local) Scorer Tree Ensemble Learner(Regression) Tree Ensemble Predictor(Regression) Tree EnsembleLearner Tree EnsemblePredictor

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: Adrian Nembach
Created at: 2015-12-08
On NodePit since: 2019-07-12
Last update: 2024-04-20
Created with KNIME version: v4.1.0

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