RANDOM FOREST is a combination of an ensemble method (BAGGING) and a particular decision tree algorithm (“Random Tree” into TANAGRA).
In this tutorial, we use the HEART (UCI Machine Learning Repository). We aim to predict a heart disease from various descriptors such as the age of the patient, etc. We have already used this dataset in other tutorials (see http://data-mining-tutorials.blogspot.com/search?q=heart).
Keywords: random forest, ensemble methods, decision tree, cross-validation
Components: Bagging, Rnd Tree, Supervised Learning, Cross-validation, C4.5
Tutorial: en_Tanagra_Random_Forest.pdf
Dataset: dr_heart.bdm
Reference: L. Breiman, A. Cutler, « Random Forests ».
