Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach ...

We developed a modeling approach for seasonal streamflow forecasts using a machine learning technique, random forest (RF), for runoff season flows (April 1–July 31 total) at the important gauge of ...

There is an increasing need for skillful streamflow forecasts that extend beyond a 12-month lead time (“year-2 forecasts”), particularly in basins with highly variable interannual streamflow, large ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the random forest regression technique (and a variant called bagging regression), where the goal is to ...

A: A random forest is a machine-learning method that makes predictions by combining the decisions of many simpler models called decision trees. A decision tree works like a tree from bottom-up. At ...