Machine Learning Algorithms

Random forests

A random forest is a set of decision trees built on random samples with a different policy for splitting a node: Instead of looking for the best choice, in such a model, a random subset of features (for each tree) is used, trying to find the threshold that best separates the data. As a result, there will be many trees trained in a weaker way and each of them will produce a different prediction.

There are two ways to interpret these results; the more common approach is based on a majority vote (the most voted class will be considered correct). However, scikit-learn implements an algorithm based on averaging the results, which yields very accurate predictions. Even if they are theoretically different, the probabilistic average ...