Lectures /
Random ForestsRandom forestsGiven {$m$} observations with {$p$} predictors. Input:
Repeat k times:
To predict, take the modal classification (‘majority vote’) over all the trees or the average of the real values across the trees. Mathworks calls their random forest function “TreeBagger” See also wikipedia Variation: Extremely Random Forests“As in random forests, a random subset of candidate features is used, but instead of looking for the most discriminative thresholds, thresholds are drawn at random for each candidate feature and the best of these randomly-generated thresholds is picked as the splitting rule. ” |