What is a Random Forest?
A random forest approach is a modern, state of the art machine learning approach. A random forest consists of 500 different uncorrelated decision trees.
A decision tree is a hierarchical tree-like model of decisions. The decision tree has different splitting paths which are followed according to certain decision rules. At the end of the tree are the leaves which represent the final classification of the decision tree. One problem of using a single decision tree for classification is that the prediction is either 100 % correct or 100 % incorrect.
Therefore, a random forest uses 500 different decision trees. During training each tree has grown in a randomized way and uses different parameters for classification. For the final classification each tree gives a classification and the final index provides the percentage of trees with a certain classification.
Video: Watch Prof Ambrósio’s explanation of Random Forest here (Englisch)
