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Tree_​Ensembles

Workflow

Random Forest, Gradient Boosted Trees, and TreeEnsemble
classificationmachine learningpredictionanalyticsKNIMEdecision treedata scienceC4.5CARTrandom forestbaggingensemble models
Three advanced algorithms (random forest, tree ensemble, and gradientboosted trees) are trained on the adult dataset to predict the feature"income" Custom Bagging and Boosting models 75% for training 25 % for testing50 dec treesmin node size = 2Read the adult dataset50 models Partitioning Boosting Learner Boosting Predictor Bagging Random ForestLearner Random ForestPredictor Scorer (JavaScript) File Reader Gradient BoostedTrees Learner Gradient BoostedTrees Predictor Scorer (JavaScript) Tree EnsembleLearner Tree EnsemblePredictor Scorer (JavaScript) Three advanced algorithms (random forest, tree ensemble, and gradientboosted trees) are trained on the adult dataset to predict the feature"income" Custom Bagging and Boosting models 75% for training 25 % for testing50 dec treesmin node size = 2Read the adult dataset50 models Partitioning Boosting Learner Boosting Predictor Bagging Random ForestLearner Random ForestPredictor Scorer (JavaScript) File Reader Gradient BoostedTrees Learner Gradient BoostedTrees Predictor Scorer (JavaScript) Tree EnsembleLearner Tree EnsemblePredictor Scorer (JavaScript)

Download

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Nodes

Tree_​Ensembles consists of the following 25 nodes(s):

Plugins

Tree_​Ensembles contains nodes provided by the following 4 plugin(s):