Building a Random Forest

Random Forest models provide attribute importance ranking and are built using existing Oracle Machine Learning for SQL APIs.

Random forest models provide attribute importance ranking of predictors. The model is built by specifying parameters in the existing APIs. The scoring is performed using the same SQL queries and APIs as the existing classification algorithms. Oracle Machine Learning for SQL implements a variant of classical Random Forest algorithm. This implementation supports big data sets. The implementation of the algorithm differs in the following ways:

• Oracle Machine Learning for SQL does not support bagging and instead provides sampling without replacement

• Users have the ability to specify the depth of the tree. Trees are not built to maximum depth.

Note:

The term hyperparameter is also interchangeably used for model setting.