Learns a random forest* (an ensemble of decision trees) for regression. Each of the regression tree models is learned on a different set of rows (records) and/or a different set of columns (describing attributes), whereby the latter can also be a bit-vector or byte-vector descriptor (e.g. molecular fingerprint). The output model describes an ensemble of regression tree models and is applied in the corresponding predictor node using a simply mean of the individual predictions.
For a more general description and suggested default parameters see the node description of the classification Random Forest Learner node.
This node provides a subset of the functionality of the Tree Ensemble Learner (Regression). If you need additional functionality, please check out the Tree Ensemble Learner (Regression)
(*) RANDOM FORESTS is a registered trademark of Minitab, LLC and is used with Minitab’s permission.
Options
Attribute Selection
Tree Options
Ensemble Configuration
Input Ports
- The data to learn from. It must contain at least one numeric target column and either a fingerprint (bit-vector/byte-vector) column or another numeric or nominal column.
Output Ports
- The input data with the out-of-bag response estimates, i.e. for each input row the mean output of all models that did not use the row in the training. If the entire data was used to train the individual models then this output will contain the input data with missing response and response variance values. The appended columns are equivalent to the columns appended by the corresponding predictor node. There is one additional column model count, which contains the number of models used for the voting (number of models not using the row throughout the learning.)
- A statistics table on the attributes used in the different tree learners. Each row represents one training attribute with these statistics: #splits (level x) as the number of models, which use the attribute as split on level x (with level 0 as root split); #candidates (level x) is the number of times an attribute was in the attribute sample for level x (in a random forest setup these samples differ from node to node). If no attribute sampling is used #candidates is the number of models. Note, these numbers are uncorrected, i.e. if an attribute is selected on level 0 but is also in the candidate set of level 1 (but will not be split on level 1 because it has been split one level up), the #candidate number will still count the attribute as candidate.
- The trained model.
Popular Successors
Random Forest Predictor (Regression)80 %- Math Formula4 %
Tree Ensemble Model Extract2 %- Interactive Table (legacy)2 %
Scorer1 %Random Forest Predictor1 %Model Writer1 %- Model Loop End1 %
- Model to Cell< 1 %
CSV Writer< 1 %- X-Aggregator< 1 %
- 2D/3D Scatterplot< 1 %
- Decision Tree Learner< 1 %
Tree Ensemble Predictor< 1 %Tree Ensemble Predictor (Regression)< 1 %Random Forest Learner< 1 %String To Number< 1 %Linear Correlation< 1 %Double To Int< 1 %- Column Filter< 1 %
- GroupBy< 1 %
- Column Splitter< 1 %
- Single sample t-test< 1 %
- Scatter Plot (legacy)< 1 %
- Scatter Matrix (legacy)< 1 %
- ROC Curve (legacy)< 1 %
- Boosting Learner Loop End< 1 %
XLS Writer< 1 %- Parameter Optimization Loop End< 1 %
XML Writer< 1 %RMSE Calculator< 1 %
Views
- Tree Views
- An decision tree viewer for all the trained models. Use the spinner to iterate through the different models.
Workflows
- No workflows found
Links
- No links available
Developers
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