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KNIME GmbH tree ensemble learner (regression) node
Tree Ensemble Learner (Regression) Node, supplied by KNIME GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/tree+ensemble+learner+node/tree+ensemble+learner/pm28651433-84-6-12
Average 90 stars, based on 1 article reviews
tree ensemble learner (regression) node - by Bioz Stars, 2026-10
90/100 stars

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Article Title: ALPACA: A machine Learning Platform for Affinity and selectivity profiling of CAnnabinoids receptors modulators.
Article Snippet: Finally, we developed the selectivity model (i.e., built on CBRs-qsarSEL data), based on the RF algorithm and using the Tree Ensemble Learner/predictor provided by the KNIME space.

Article Title: Understanding the Role of the Microbiome in Cancer Diagnostics and Therapeutics by Creating and Utilizing ML Models
Article Snippet: Referring to the performance metrics of the decision tree approach, we proceed to explore the ensemble-based algorithms (Scikit-learn random forest classifier in Python and tree ensemble learner in KNIME), building multiple decision trees and taking advantage of the tree-related majority voting.

Article Title: SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19
Article Snippet: Similarly, we used the KNIME node “Tree Ensemble Learner” to develop the TE model.

Article Title: Understanding the Role of the Microbiome in Cancer Diagnostics and Therapeutics by Creating and Utilizing ML Models
Article Snippet: The tree ensemble learner in KNIME provides a statistics table on the different decision trees’ attributes (output ports).

Article Title: ALPACA: A machine Learning Platform for Affinity and selectivity profiling of CAnnabinoids receptors modulators.
Article Snippet: We employed the following KNIME nodes: Tree Ensemble Learner/predictor, K Nearest Neighbor, Gradient Boosted Trees Learner/predictor, and RProp MLP Learner/predictor to develop RF, KNN, GB, and MLP classifiers [33].

Article Title: A survey on computational taste predictors
Article Snippet: A Random Forest with Tree Ensemble Learner and Predictor nodes in KNIME [ ] was implemented, and a Bayesian-based features detection was applied to analyze the important and frequent features.



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