Random Forest Regression

This operator implements the Random Forest Regression algorithm from Spark MLlib.

Random_Forest_regression_icon

Information at a Glance

Note: This operator can only be used with TIBCO® Data Virtualization and Apache Spark 3.2 or later.

Parameter	Description
Category	Model
Data source type	TIBCO® Data Virtualization
Send output to other operators	Yes
Data processing tool	TIBCO® DV, Apache Spark 3.2 or later

Algorithm

The Random Forest Regression is an ensemble tree algorithm that generates numerical predictions by averaging the numerical regression tree predictions of the ensemble. You can fine-tune the hyper-parameters of interest with the cross-validation training method. The operator uses the specified metric to evaluate the performance. The output of the operator is the model object with the best validation performance. This operator implements the Random Forest Regression algorithm from Spark MLlib.

Input

An input is a single tabular data set.

Bad or Missing Values

Null values are not allowed and result in an error.

Configuration

The following table provides the configuration details for the Random Forest Regression operator.

Parameter	Description
Notes	Notes or helpful information about this operator's parameter settings. When you enter content in the Notes field, a yellow asterisk appears on the operator.
Dependent Variable	Specify the data column as a dependent column. It must be a continuous numerical variable.
Use all available columns as Predictors	When set to Yes, the operator uses all the available columns as predictors and ignores the Continuous Predictors and Categorical Predictors parameters. When set to No, the user must select at least one of the Continuous or Categorical Predictors.
Continuous Predictors	Specify the numerical data columns as independent columns. It must be numerical column. Click Select Columns to select the required columns. Note: The columns selected in the Categorical Predictors parameter are not available.
Categorical Predictors	Specify the categorical data columns as independent columns. Note: The columns selected in the Continuous Predictors parameter are not available.
Evaluation Metric	Specify the metric for evaluating the regression models. The following values are available: MSE MSE R2 RMSE For more information, see the Apache Spark documentation on Classification and Regression. Default: RMSE
Number of Feature Functions	Specify the function to determine the number of features for building each decision tree. The following values are available: All ⅓ Square Root log2 User Defined Default: Square Root
Feature Sampling Ratio	Specify the fraction of a number of features per node to use when the Number of Feature Functions is set to the User Defined option. The input for this parameter should be a comma-separated sequence of numeric values in `(0,1)`. Default: `0.5,0.7` Note: If User Defined is not selected in the Number of Feature Functions, then this parameter is ignored.
Max Depth	Specify the maximum depth of each tree. The input for this parameter should be a comma-separated sequence of integer values from 0 to 30. Default: `3,5`
Number of Trees	Specify the total number of trees. The input for this parameter should be a comma-separated sequence of integer values. Default: `10,100`
Row Sampling Ratio	Specify the fraction of training data for building each decision tree. The input for this parameter should be a comma-separated sequence of double values in `(0,1)`. Default: `1`
Min Leaf Size	Specify the smallest number of data instances within a decision tree's terminal leaf node. The input for this parameter should be a comma-separated sequence of integer values (for example, `1,2`). Default: `1`
Max Bins	Specify the maximum number of bins used for discretizing and splitting continuous features. The input for this parameter should be a comma-separated sequence of integer values (for example, 256). Note: The value of Max Bins should be larger than the number of unique levels of any selected categorical columns. Default: `32`
Number of Cross Validation Folds	Specify the number of cross-validation samples. Default: `3`
Random Seed	The seed used for the pseudo-random generation. Default: `1`

Output

Visual Output

Parameter Summary Info: Displays information about the input parameters and their current settings.
Variable Importance: Displays the importance of predictors as evaluated in the training process. For each predictor, the variable importance for the model is displayed in the second column. This provides an indication of the importance or impact of a particular parameter on the model's predictions.
Training Summary: Displays a table with a row for each tested combination of hyper-parameters. For each row, the chosen metric is displayed and the Best Model is marked. The information provides an insight into the parameters which resulted in the best model.

Output to successive operators

A model object that can be used with a Predictor operator. One additional column is produced in the Predictor operator: