Watch Machine Learning Algorithms: Random Forest

Provides steps for applying random forest to do classification and prediction. Includes why and when it is used, benefits, number of trees, number of variables tried at each step, data partitioning, prediction and confusion matrix, accuracy and sensitivity, randomForest & caret packages, bootstrap samples and out of bag error, and variable importance. Detailed example with cardiotocographic data.