Learns 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/double-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 the selected aggregation mode to aggregate the votes of the individual decision trees.
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).
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.
The tree ensemble nodes now also support binary splits for nominal columns. Depending on the kind of problem (two- or multi-class) different algorithms are implemented to enable the efficient calculation of splits.
- 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
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 (bit/byte/double 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 Predecessors
- X-Partitioner13 %
- Equal Size Sampling10 %
Random Forest Learner6 %- SMOTE6 %
Row Filter (deprecated)4 %- Row Sampling4 %
String To Number3 %- Color Manager3 %
Scorer2 %- Normalizer2 %
Table Reader2 %Random Forest Predictor2 %Tree Ensemble Learner2 %- Statistics2 %
Table Row to Variable1 %- Reference Column Filter1 %
- Decision Tree Predictor1 %
- Missing Value1 %
PCA Apply1 %- Correlation Filter1 %
Joiner1 %- Target Shuffling1 %
- IF Switch1 %
- Tree Ensemble Model Extract< 1 %
- One to Many< 1 %
Row Splitter (deprecated)< 1 %- Shuffle< 1 %
Merge Variables< 1 %Table Row To Variable Loop Start< 1 %CSV Reader< 1 %- Bootstrap Sampling< 1 %
- Reference Row Filter< 1 %
- Rule-based Row Filter< 1 %
Excel Reader (XLS)< 1 %- CSV Reader< 1 %
- Table Creator< 1 %
Gradient Boosted Trees Predictor< 1 %- Fingerprints< 1 %
- Chunk Loop Start< 1 %
- RDKit Fingerprint< 1 %
- Category to Number< 1 %
- Domain Calculator< 1 %
- Column Filter< 1 %
- RowID< 1 %
- Boosting Learner Loop Start< 1 %
- Outlier Removal< 1 %
- Fingerprints Expander< 1 %
Logistic Regression Learner< 1 %Column Appender< 1 %- GroupBy< 1 %
- String Manipulation< 1 %
- Table Transposer< 1 %
Color Appender< 1 %XLS Reader< 1 %- Document Data Extractor< 1 %
- Weka Predictor (3.7)< 1 %
Backward Feature Elimination Start (2:2)< 1 %- Feature Selection Loop Start (1:1)< 1 %
- Denormalizer< 1 %
Table Column to Variable< 1 %File Reader< 1 %- Java Edit Variable< 1 %
- Java Snippet< 1 %
- Column List Loop Start< 1 %
- Hierarchical Cluster Assigner (local)< 1 %
PCA< 1 %Tree Ensemble Predictor< 1 %- Concatenate< 1 %
- Auto-Binner< 1 %
- Column Comparator< 1 %
Number To String< 1 %- Missing Value Column Filter< 1 %
- Partitioning< 1 %
- Column Splitter< 1 %
- Rule Engine< 1 %
- Rule-based Row Splitter< 1 %
- Data Generator< 1 %
- Parallel Chunk Start< 1 %
Excel Reader (XLS)< 1 %- Category to Class< 1 %
- Keygraph Keyword Extractor< 1 %
- Column Selection (legacy)< 1 %
- JSON to Table< 1 %
- Parameter Optimization Loop Start< 1 %
Python Script (1⇒1)< 1 %- R Snippet< 1 %
- Range Splitter< 1 %
- Split Collection Column< 1 %
- Try (Data Ports)< 1 %
Database Connection Table Reader< 1 %Line Reader< 1 %Backward Feature Elimination Start (1:1)< 1 %- Feature Selection Loop End< 1 %
Counting Loop Start< 1 %- Group Loop Start< 1 %
- Fuzzy c-Means< 1 %
K Nearest Neighbor< 1 %PCA Compute< 1 %- Linear Regression Learner< 1 %
- Association Rule Learner< 1 %
- Random Forest Learner (Regression)< 1 %
- Random Forest Predictor (Regression)< 1 %
Concatenate (Optional in)< 1 %Cell Splitter< 1 %- Constant Value Column< 1 %
Linear Correlation< 1 %- Double to Integer< 1 %
- Low Variance Filter< 1 %
- Normalizer (Apply)< 1 %
- TimeDelay Loop Start< 1 %
- Math Formula (Variable)< 1 %
Excel Reader (XLS)< 1 %- Lift Chart (JavaScript)< 1 %
- File Reader< 1 %
Views
- Tree Views
- An decision tree viewer for all the trained models. Use the spinner to iterate through the different models.
Workflows
02_Text_Classification_from_Forum_PostsKNIME Hub- 02_Text_Classification_from_Forum_PostsKNIME Hub
- 03_Bioactivity_Prediction_Learn_All_MethodsKNIME Hub
- 03_Bioactivity_Prediction_Learn_TestingKNIME Hub
- Guided Model SelectionKNIME Hub
- Show all 8 workflows
Links
- No links available
Developers
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