Learns a random forest*, which consists of a chosen number of decision trees. Each of the decision tree models is learned on a different set of rows (records) and 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 row sets for each decision tree are created by bootstrapping and have the same size as the original input table. For each node of a decision tree a new set of attributes is determined by taking a random sample of size sqrt(m) where m is the total number of attributes. The output model describes a random forest and is applied in the corresponding predictor node using a simple majority vote.
This node provides a subset of the functionality of the Tree Ensemble Learner corresponding to a random forest. If you need additional functionality please check out the Tree Ensemble Learner.
Experiments have shown the results on different data sets are very similar to the random Forest implementation available in R.
The decision tree construction takes place in main memory (all data and all models are kept in memory).
The missing value handling corresponds to the method described here. The basic idea is to try for each split to send the missing values in every direction and the one yielding the best results (i.e. largest gain) is then used. If no missing values are present during training, the direction of a split that the most records are following is chosen as direction for missing values during testing.
Nominal columns are split in a binary manner. The determination of the split depends on the kind of problem:
- For two-class classification problems the method described in section 9.4 of "Classification and Regression Trees" by Breiman et al. (1984) is used.
- For multi-class classification problems the method described in "Partitioning Nominal Attributes in Decision Trees" by Coppersmith et al. (1999) is used.
(*) RANDOM FORESTS is a registered trademark of Minitab, LLC and is used with Minitab’s permission.
Options
Input Ports
- The data to learn from. It must contain at least one nominal 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 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 Successors
Scorer19 %Model Writer10 %- Tree Ensemble Model Extract5 %
- ROC Curve (legacy)5 %
- Random Forest Predictor (Regression)4 %
- Tree Ensemble Statistics4 %
- Math Formula4 %
Tree Ensemble Predictor3 %- Boosting Learner Loop End3 %
- Model to Cell3 %
CSV Writer3 %Tree Ensemble Learner2 %- Random Forest Distance2 %
- Interactive Table (legacy)2 %
Random Forest Learner2 %- X-Aggregator1 %
- Decision Tree Predictor1 %
- Random Forest Learner (Regression)1 %
- Column Filter1 %
Variable to Table Row1 %- Model Loop End1 %
- Decision Tree Learner< 1 %
- Rule-based Row Filter< 1 %
- Scatter Plot (legacy)< 1 %
Excel Writer (XLS)< 1 %Joiner< 1 %Table Writer< 1 %- Domain Calculator< 1 %
Row Filter (deprecated)< 1 %- Tree Ensemble Predictor (Regression)< 1 %
- GroupBy< 1 %
- Hierarchical Clustering (DistMatrix)< 1 %
- Decision Tree to Image< 1 %
Numeric Scorer< 1 %String To Number< 1 %- Lift Chart (legacy)< 1 %
- Color Manager< 1 %
- Weka Predictor (3.7)< 1 %
Binary Scorer< 1 %- Java Snippet< 1 %
Random Forest Predictor< 1 %Linear Correlation< 1 %- Partitioning< 1 %
- R Plot< 1 %
- Linear Regression Learner< 1 %
- Decision Tree View (JavaScript)< 1 %
- Table View (JavaScript)< 1 %
Number To String< 1 %- Statistics< 1 %
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- ROC Curve (JavaScript)< 1 %
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Gradient Boosted Trees Learner< 1 %Gradient Boosted Trees Learner (Regression)< 1 %- Simple Regression Tree Predictor< 1 %
Excel Sheet Appender (XLS)< 1 %- Parameter Optimization Loop End< 1 %
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K Nearest Neighbor< 1 %- MDS (DistMatrix)< 1 %
Logistic Regression Learner< 1 %- SVM Learner< 1 %
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- Pie chart (legacy)< 1 %
- DL4J Feedforward Predictor (Classification) (legacy)< 1 %
XLS Sheet Appender< 1 %XLS Writer< 1 %- Text Classifier Learner< 1 %
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Table Column to Variable< 1 %Database Writer< 1 %Backward Feature Elimination End< 1 %Loop End< 1 %- Fuzzy Rule Predictor< 1 %
- MultiLayerPerceptron Predictor< 1 %
- RProp MLP Learner< 1 %
PCA Compute< 1 %- Logistic Regression Learner< 1 %
Gradient Boosted Trees Predictor< 1 %- Tree Ensemble Learner (Regression)< 1 %
- Add Empty Rows< 1 %
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Concatenate (Optional in)< 1 %- Auto-Binner< 1 %
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Number To String (PMML)< 1 %Column Rename< 1 %- Row Sampling< 1 %
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- Rule-based Row Splitter< 1 %
- Ruleset Predictor< 1 %
Rank Correlation< 1 %- Extract Table Dimension< 1 %
Merge Variables< 1 %- Timer Info< 1 %
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- Histogram (legacy)< 1 %
- Interactive Histogram (legacy)< 1 %
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K Nearest Neighbor (Distance Function)< 1 %- Line Chart (JFreeChart)< 1 %
- Scatter Plot (JFreeChart)< 1 %
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- Multiobjective Score Computation< 1 %
- R Learner< 1 %
- R View (Table)< 1 %
- Result< 1 %
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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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