Random Forest Template for Spotfire® 2.0

Overview

Random Forest is a machine-learning algorithm that aggregates the predictions from many decision trees on different subsets of data.  This technique allows the model to be more accurate than single decision trees in predicting new data.  It is a supervised learning technique that can be used to determine variable importance and make predictions.  This point-and-click template uses a distributed random forest trained in H2O for best in the market training performance.  The response can be either numeric or binary (e.g., good / bad) and predictors can be a mixture of numeric and categorical columns.  Version 2 features automated machine learning to optimize model tuning parameters.

Details

There is a community article describing details of the methodology.