Learns a random forest* (an ensemble of decision trees) for regression. Each of the decision tree models is built with a different set of rows (records) and for each split within a tree a randomly chosen set of columns (describing attributes) is used. The row sets for each decision tree are created by bootstrapping and have the same size as the original input table. The attribute set for an individual split in a decision tree is determined by randomly selecting sqrt(m) attributes from the available attributes where m is the total number of learning columns. 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.
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 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
Input Ports
- The data to learn from. They 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 predictions, i.e. for each input row the mean and variance of outputs of all models that did not use the row for training. 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.) 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 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 will still count the attribute as candidate.
- The trained model.
Popular Predecessors
- Partitioning12 %
- Parameter Optimization Loop Start5 %
CSV Reader4 %String To Number3 %- X-Partitioner2 %
Excel Reader (XLS)2 %- Normalizer2 %
- Column Filter2 %
Table Row to Variable2 %Number To String2 %Random Forest Learner1 %Table Row To Variable Loop Start1 %- Scorer1 %
Table Row to Variable1 %- Bootstrap Sampling1 %
- Reference Row Filter< 1 %
- R Snippet< 1 %
Interval Loop Start< 1 %- Correlation Filter< 1 %
- Random Forest Learner< 1 %
Row Splitter (deprecated)< 1 %- ARFF Reader< 1 %
Random Forest Predictor< 1 %- One to Many< 1 %
- Normalizer (Apply)< 1 %
- Math Formula (Multi Column)< 1 %
- Conformal Partitioning< 1 %
- Category to Number< 1 %
- Domain Calculator< 1 %
- Missing Value< 1 %
- Statistics< 1 %
CASE Switch Data (Start)< 1 %- RDKit Fingerprint< 1 %
- Feature Selection Loop Start (1:1)< 1 %
- Duplicate Row Filter< 1 %
- Parameter Optimization Loop Start (Table)< 1 %
PCA Apply< 1 %Column Appender< 1 %- Rule Engine< 1 %
- Number to String< 1 %
- Double to Integer< 1 %
- Column List Loop Start< 1 %
- Polynomial Regression Learner< 1 %
Numeric Scorer< 1 %- Random Forest Predictor (Regression)< 1 %
- RowID< 1 %
- Shuffle< 1 %
- Sorter< 1 %
- String Manipulation< 1 %
- Random Forest Predictor< 1 %
Linear Correlation< 1 %- Scatter Plot (JavaScript)< 1 %
- Normalizer (PMML)< 1 %
- Merge Variables< 1 %
- Fingerprints Expander< 1 %
PCA< 1 %- Concatenate< 1 %
- Expand Bit Vector< 1 %
Row Filter (deprecated)< 1 %Joiner< 1 %- Column Filter (legacy)< 1 %
- Breakpoint< 1 %
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- SMOTE< 1 %
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String To Number (PMML)< 1 %Column Rename< 1 %- Row Sampling< 1 %
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Excel Reader (XLS)< 1 %- Table View (JavaScript)< 1 %
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- Column Appender< 1 %
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Table Column to Variable< 1 %Table Reader< 1 %File Reader< 1 %- Table Creator< 1 %
Backward Feature Elimination Start (1:1)< 1 %Loop End< 1 %- Tree Ensemble Learner (Regression)< 1 %
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Model Reader< 1 %- Group Loop Start< 1 %
Variable Loop End< 1 %- Numeric Binner< 1 %
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XLS Reader< 1 %Excel Reader (XLS)< 1 %- Fingerprint From String< 1 %
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- Regression Predictor< 1 %
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Tree Ensemble Predictor< 1 %- Number to Category (Apply)< 1 %
- Column Rename (Regex)< 1 %
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- Row Filter (Labs)< 1 %
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- Data Generator< 1 %
- Interactive Table (legacy)< 1 %
- Parallel Chunk End< 1 %
Extract Date&Time Fields< 1 %- Molecular Properties< 1 %
- Model Writer< 1 %
- Line Plot (JavaScript)< 1 %
Active Learn Loop Start< 1 %Table Column to Variable< 1 %- Java Edit Variable< 1 %
- Java Snippet< 1 %
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PCA Compute< 1 %Scorer< 1 %- SVM Predictor< 1 %
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- Denormalizer< 1 %
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Merge Variables< 1 %- Java Snippet (simple)< 1 %
- Document Vector< 1 %
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- Dense To Sparse< 1 %
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CSV Writer< 1 %Database Reader< 1 %Backward Feature Elimination Filter< 1 %Recursive Loop Start (2 ports)< 1 %Recursive Loop Start< 1 %- Cluster Assigner< 1 %
- Fuzzy c-Means< 1 %
- K Nearest Neighbor< 1 %
- Logistic Regression Learner< 1 %
- PCA Compute< 1 %
Tree Ensemble Learner< 1 %- Tree Ensemble Predictor< 1 %
- PMML Simple Regression Tree Predictor< 1 %
- Tree Ensemble Statistics< 1 %
- Expand Byte Vector< 1 %
- Column Auto Type Cast< 1 %
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- Extract Missing Value Cause< 1 %
- Interactive HiLite Collector< 1 %
- Nominal Value Row Splitter< 1 %
- Denormalizer (PMML)< 1 %
- String Splitter (Regex)< 1 %
- Number Rounder< 1 %
- Ruleset Predictor< 1 %
Rank Correlation< 1 %- Empty Table Switch< 1 %
CASE Switch Data (End)< 1 %- IF Switch< 1 %
- Box Plot (legacy)< 1 %
- Molecule Type Cast< 1 %
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- Table to JSON< 1 %
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- Date&Time Difference< 1 %
- Date Field Extractor (legacy)< 1 %
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- RDKit Generate Coords< 1 %
- Fingerprint CONCATENATE< 1 %
- Table Row to Variable Loop Start< 1 %
- PNN Learner (DDA)< 1 %
- PMML Gradient Boosted Trees Predictor (Regression)< 1 %
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- Tree Ensemble Predictor (Regression)< 1 %
- Column Comparator< 1 %
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Python Script (1⇒1)< 1 %- Date&Time Part Extractor< 1 %
- Window Loop Start< 1 %
- Fingerprint Similarity< 1 %
Views
- Tree Views
- A decision tree viewer for all the trained models. Use the spinner to iterate through the different models.
Workflows
01.RF_SonKNIME Hub- 02_AutoML_Regression_and_Classification_ExamplesKNIME Hub
- 02_AutoML_Regression_and_Classification_ExamplesKNIME Hub
- 02_Learning_a_Random_ForestKNIME Hub
- 02_SHAP_and_Shapley_ValuesKNIME Hub
- Show all 317 workflows
Links
- No links available
Developers
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