Random Forest Predictor

This Node Is Deprecated — This version of the node has been replaced with a new and improved version. The old version is kept for backwards-compatibility, but for all new workflows we suggest to use the version linked below.
Go to Suggested ReplacementRandom Forest Predictor

Predicts patterns according to an aggregation of the predictions of the individual trees in a random forest* model.

(*) RANDOM FORESTS is a registered trademark of Minitab, LLC and is used with Minitab’s permission.


Change prediction column name
Select if you want to change the name of the column containing the prediction.
Prediction column name
The name of the column that will contain the prediction of the tree ensemble model
Append overall prediction confidence
The confidence of the predicted class. It is the maximum of all confidence values (which can be appended separately).
Append individual class probabilities
For each class the prediction confidence. It's the number of trees predicting to the current class (as per column name) divided by the total number of trees.
Suffix for probability columns
Here a suffix for the names of the class probability columns can be entered.
Use soft voting
Per default ("hard voting") the class that receives the most votes is predicted. In case of "soft voting" the class probabilities of all trees are aggregated and the class with the highest aggregated probability is predicted. In order for this to work properly, the random forest model needs to contain the class distributions. This can be specified in the learner node by selecting the option "Save target distribution in tree nodes". Setting this option on models that do not have the target distributions saved, will cause a warning message to be issued.

Input Ports

The output of the learner.
Data to be predicted.

Output Ports

Input data along with prediction columns.


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