Random 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.

Options

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.

Views

This node has no views

Workflows

• 01_AnalyticsKNIME Hub
• 01_Emil_the_TeacherBotKNIME Hub
• 01_Model_Deployment_file_to_database_schedulingKNIME Hub
• 03_AL_Training_Subset_Uncertain_ClassesKNIME Hub
• 03_Bioactivity_Prediction_Learn_All_MethodsKNIME Hub
• Show all 10 workflows

Links

• No links available

Developers