Metodebevisregistrering

Random Forest

Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.

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Random Forest (Breiman Ensemble of Decision Trees)

Taksonomisk metoderegistrering · ml-model / machine-learning

Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. · DOI 10.1023/A:1010933404324
James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning (Ch. 8). Springer. · ISBN 978-1-4614-7138-7

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Same method familyDecision Treemachine-suggested · Relational suggestion, not evidence.See alsoLogistic Regressionmachine-suggested · Relational suggestion, not evidence.Same method familySupport Vector Machinemachine-suggested · Relational suggestion, not evidence.Same method familyXGBoostmachine-suggested · Relational suggestion, not evidence.