Random Forest
The Random Forest algorithm creates an ensemble model of decision trees. Each tree is trained on a randomly selected subset of the training data. This supervised learning method has a number of applications, including:
- Predicting genetic outcomes
- Financial analysis
- Medical diagnosis
You can use the following functions to train the Random Forest model, and use the model to make predictions on a set of test data:
For a complete example of how to use the Random Forest algorithm in Vertica, see Classifying Data Using Random Forest.