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 ReplacementTree Ensemble LearnerLearns an ensemble of decision trees (such as random forest* variants). Each of the decision 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/byte-vector descriptor (e.g. molecular fingerprint). The output model describes an ensemble of decision tree models and is applied in the corresponding predictor node using a simply majority vote.
The following configuration settings learn a model that is similar to the random forest™ classifier described by Leo Breiman and Adele Cutler:
- Tree Options - Split Criterion: Gini Index
- Tree Options - Limit number of levels (tree depth): unlimited
- Tree Options - Minimum node size: unlimited
- Ensemble Configuration - Number of models: Arbitrary (random forest arguably does not overfit)
- Ensemble Configuration - Data Sampling: Use all rows (fraction = 1) but choose sampling with replacement (bootstrapping)
- Ensemble Configuration - Attribute Sampling: Sample using a different set of attributes for each tree node split; usually square root of number of attributes but can vary
The decision tree construction takes place in main memory (all data and all models are kept in memory).
(*) 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 nominal target column and either a fingerprint (bitvector) column or another numeric or nominal column.
Output Ports
- The input data with the out-of-bag error estimates, i.e. for each input row the majority vote 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 prediction and confidence 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 Predecessors
- Missing Value10 %
- Parameter Optimization Loop Start9 %
Random Forest Learner7 %File Reader5 %- Domain Calculator5 %
- Decision Tree to Ruleset5 %
Scorer3 %- SMOTE3 %
- Concatenate3 %
Random Forest Predictor3 %- Color Manager3 %
- Reference Column Filter2 %
- Partitioning2 %
- Shuffle2 %
- Data Generator2 %
- Decision Tree Predictor1 %
Number To String1 %- Correlation Filter1 %
- Normalizer1 %
Tree Ensemble Model Extract1 %- Auto-Binner1 %
- Missing Value Column Filter1 %
- Extract Variables (Data)< 1 %
Table Column to Variable< 1 %CSV Reader< 1 %Line Reader< 1 %Backward Feature Elimination Filter< 1 %Backward Feature Elimination Start (1:1)< 1 %Counting Loop Start< 1 %- Chunk Loop Start< 1 %
- X-Partitioner< 1 %
- k-Means< 1 %
PCA Apply< 1 %Cell Splitter< 1 %- Number to Category (Apply)< 1 %
String To Number< 1 %- Column Filter< 1 %
- Low Variance Filter< 1 %
- Normalizer (Apply)< 1 %
- Normalizer Apply (PMML)< 1 %
- Row Sampling< 1 %
- Target Shuffling< 1 %
- Rule-based Row Filter< 1 %
- Statistics< 1 %
- Math Formula< 1 %
XLS Reader< 1 %- Chunk Loop Run-for-time Loop Start< 1 %
- ARFF Reader< 1 %
Tree Ensemble Learner< 1 %Tree Ensemble Predictor< 1 %- Numeric Binner< 1 %
- Bootstrap Sampling< 1 %
- Edit Nominal Domain< 1 %
- Equal Size Sampling< 1 %
- Reference Row Splitter< 1 %
- GroupBy< 1 %
- Denormalizer< 1 %
- Missing Value (Apply)< 1 %
- RowID< 1 %
- Numeric Row Splitter< 1 %
- Parallel Chunk End< 1 %
- Conditional Label Assigner< 1 %
- Boosting Learner Loop End< 1 %
- String to Date&Time< 1 %
Views
- Tree Views
- An decision tree viewer for all the trained models. Use the spinner to iterate through the different models.
Workflows
Links
- No links available
Developers
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