Random forests are an excellent “out of the box” tool for machine learning with many of the same advantages that have made neural nets so popular. They are able to capture non-linear and non-monotonic functions, are invariant to the scale of input data, are robust to missing values, and do “automatic” feature extraction. Additionally, they have other benefits that neural nets do not. What follows is a look into how random forests work, how they may be usefully applied, and a discussio...
