Learns an ensemble of regression trees (such as random forest* variants). Typically, each tree is built with a different set of rows (records) and/or columns (attributes). See the options for Data Sampling and Attribute Sampling for more details. The attributes can also be provided as bit (fingerprint), byte, or double vector. The output model describes an ensemble of regression tree models and is applied in the corresponding predictor node using a simple mean of the individual predictions.
In a regression tree the predicted value for a leaf node is the mean target value of the records within the leaf. Hence the predictions are best (with respect to the training data) if the variance of target values within a leaf is minimal. This is achieved by splits that minimize the sum of squared errors in their respective children.
For a more general description and suggested default parameters see the node description of the classification Tree Ensemble Learner.
(*) 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/byte/double vector) column or another numeric or nominal column.
Output Ports
- The input data with the out-of-bag predictions, i.e. for each input row the mean and variance of outputs of all models that did not use the row for 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 voting (number of models not using the row throughout the learning.) The out-of-bag predictions can be used to get an estimate of the generalization ability of the random forest by feeding them into the Numeric Scorer node.
- 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 the 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 is not split on level 1 because it has been split one level up), the #candidate number still counts the attribute as a candidate.
- The trained model.
Popular Predecessors
CSV Reader6 %- Random Forest Learner (Regression)3 %
String To Number3 %Row Filter (deprecated)3 %- Target Shuffling3 %
- SMOTE2 %
- Joiner2 %
Joiner2 %- Partitioning2 %
- Rule-based Row Splitter2 %
- Numeric Scorer2 %
- Rule Engine2 %
Excel Reader (XLS)2 %- Random Forest Predictor (Regression)2 %
- Correlation Filter2 %
- Table Row to Variable2 %
Number To String2 %- Excel Reader2 %
- Number to String1 %
- CSV Reader1 %
Database Reader1 %Counting Loop Start1 %- X-Partitioner1 %
- Tree Ensemble Learner1 %
- Tree Ensemble Predictor (Regression)1 %
- Category to Number1 %
- Domain Calculator1 %
- Column Filter1 %
- Normalizer1 %
- Boosting Learner Loop Start1 %
- Math Formula1 %
- Parameter Optimization Loop Start1 %
- R Snippet1 %
- X-Aggregator1 %
- Tree Ensemble Learner (Regression)1 %
- Auto-Binner1 %
- Math Formula (Multi Column)1 %
- Scorer1 %
- Decision Tree Predictor< 1 %
- Column Resorter< 1 %
- Low Variance Filter< 1 %
- Missing Value< 1 %
- Rule-based Row Filter< 1 %
- Linear Regression Learner< 1 %
- Concatenate< 1 %
Table Reader< 1 %- Tree Ensemble Predictor< 1 %
Table Row To Variable Loop Start< 1 %- Column List Loop Start< 1 %
- K Nearest Neighbor< 1 %
Tree Ensemble Learner< 1 %- RDKit Descriptor Calculation< 1 %
- One to Many< 1 %
- H2O to Table< 1 %
- Hierarchical Clustering< 1 %
- Numeric Binner< 1 %
- Bootstrap Sampling< 1 %
- Chunk Loop Start< 1 %
- String to Number< 1 %
String To Number (PMML)< 1 %- Logistic Regression Learner< 1 %
- ARFF Reader< 1 %
File Reader< 1 %- Table Creator< 1 %
Logistic Regression Learner< 1 %- Polynomial Regression Learner< 1 %
- Gradient Boosted Trees Predictor (Regression)< 1 %
- Tree Ensemble Model Extract< 1 %
- Random Forest Learner< 1 %
Column Appender< 1 %- Equal Size Sampling< 1 %
- Reference Column Filter< 1 %
- Missing Value Column Filter< 1 %
Row Splitter (deprecated)< 1 %- Reference Row Filter< 1 %
- Denormalizer< 1 %
Column Rename< 1 %- Statistics< 1 %
- Data Generator< 1 %
- Color Manager< 1 %
Extract Date&Time Fields< 1 %- RDKit Fingerprint< 1 %
- Outlier Removal< 1 %
Table Column to Variable< 1 %- Java Snippet< 1 %
- Feature Selection Loop Start (1:1)< 1 %
- Feature Selection Loop Start (2:2)< 1 %
Generic Loop Start< 1 %Recursive Loop Start< 1 %- Naive Bayes Predictor< 1 %
- Cluster Assigner< 1 %
PCA< 1 %- Regression Predictor< 1 %
Scorer< 1 %Numeric Scorer< 1 %Random Forest Learner< 1 %- Random Forest Predictor< 1 %
- Lag Column< 1 %
- Double to Integer< 1 %
- HiLite Row Splitter< 1 %
- Reference Row Splitter< 1 %
- GroupBy< 1 %
- Number Rounder< 1 %
- Numeric Row Splitter< 1 %
- Row Sampling< 1 %
- Shuffle< 1 %
- String Replacer< 1 %
- IF Switch< 1 %
- Model Loop Start< 1 %
XLS Reader< 1 %- Category to Class< 1 %
- Weka Predictor (3.7)< 1 %
- String Input (legacy)< 1 %
- Parameter Optimization Loop End< 1 %
- R to Table< 1 %
- Fingerprints< 1 %
- Fingerprint Similarity< 1 %
- MDS< 1 %
- Gradient Boosted Trees Predictor< 1 %
- Tree Ensemble Statistics< 1 %
- Column Rename (Regex)< 1 %
- Constant Value Column< 1 %
- Duplicate Row Filter< 1 %
- Table Manipulator< 1 %
- Normalizer (PMML)< 1 %
- String to Number (PMML)< 1 %
- Document Vector< 1 %
- Path to String (Variable)< 1 %
- Table View (JavaScript)< 1 %
Views
- Tree Views
- An decision tree viewer for all the trained models. Use the spinner to iterate through the different models.
Workflows
03_Learning_a_Tree_Ensemble_ModelKNIME Hub- 04_Data_Preprocessing_for_ML_ModelsKNIME Hub
- 04_Data_Preprocessing_for_ML_ModelsKNIME Forum
- 04_Data_Preprocessing_for_ML_ModelsKNIME Hub
- 04_Data_Preprocessing_for_ML_Models1KNIME Forum
- Show all 28 workflows
Links
Developers
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