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02_​Learning_​a_​Random_​Forest

How to use the Random Forest nodes

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.

Using the Random Forest for Classification and Regression This workflow shows how to use the random forest nodes for regressionand classification tasks.It also illustrates how the Out-of-bag error estimates (the top most outportof the learner nodes) can be used to measure the accuracy of a randomforest. For advanced users:If you would like to have more options for trainingthe random forests (i.e. other row and columnsampling methods), please check out the TreeEnsemble nodes read workflow local datapartition data in 80% training20% testingusing stratified samplingscore the classificationpredictionscore the regressionpredictionrandom forests also providea table with predictions of the"Out-of-Bag" data, which can bescored to get a feeling for theaccuracy of the random forestonly keeppetal width andPrediction(petal width)draw a line plotto visualize the performanceof the simple regression treetrain random forest forclassification using class as target column andGini Index as split criterionpredict the testing datatrain Random Forest forregression usingpetal width as target columnpredict the testing data File Reader Partitioning Scorer Numeric Scorer(deprecated) Scorer Column Filter Line Plot (local) Random ForestLearner Random ForestPredictor Random Forest Learner(Regression) Random Forest Predictor(Regression) Using the Random Forest for Classification and Regression This workflow shows how to use the random forest nodes for regressionand classification tasks.It also illustrates how the Out-of-bag error estimates (the top most outportof the learner nodes) can be used to measure the accuracy of a randomforest. For advanced users:If you would like to have more options for trainingthe random forests (i.e. other row and columnsampling methods), please check out the TreeEnsemble nodes read workflow local datapartition data in 80% training20% testingusing stratified samplingscore the classificationpredictionscore the regressionpredictionrandom forests also providea table with predictions of the"Out-of-Bag" data, which can bescored to get a feeling for theaccuracy of the random forestonly keeppetal width andPrediction(petal width)draw a line plotto visualize the performanceof the simple regression treetrain random forest forclassification using class as target column andGini Index as split criterionpredict the testing datatrain Random Forest forregression usingpetal width as target columnpredict the testing dataFile Reader Partitioning Scorer Numeric Scorer(deprecated) Scorer Column Filter Line Plot (local) Random ForestLearner Random ForestPredictor Random Forest Learner(Regression) Random Forest Predictor(Regression)

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

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Created by: Adrian Nembach
Created at: 2015-12-08
On NodePit since: 2019-07-12
Last update: 2024-04-18
Created with KNIME version: v4.1.0

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