Learns a single regression tree. The procedure follows the algorithm described by "Classification and Regression Trees" (Breiman et al, 1984), whereby the current implementation applies a couple of simplifications, e.g. no pruning, not necessarily binary trees, etc.
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.
The currently used missing value handling also differs from the one used by Breiman et al, 1984. In each split the algorithm tries to find the best direction for missing values by sending them in each direction and selecting the one that yields the best result (i.e. largest gain). The procedure is adapted from the well known XGBoost algorithm and is described here .
Options
Attribute Selection
Popular Predecessors
- Partitioning9 %
Excel Reader (XLS)7 %String To Number4 %Number To String4 %- CSV Reader2 %
- Column Filter1 %
Table Reader1 %- Lag Column1 %
- Excel Reader1 %
- ARFF Reader1 %
- Column List Loop Start1 %
- Normalizer1 %
- Row Sampling1 %
Numeric Scorer1 %- Sorter< 1 %
Table Row to Variable< 1 %- Line Plot (JavaScript)< 1 %
- X-Partitioner< 1 %
- Denormalizer< 1 %
- PCA Apply< 1 %
- Decision Tree Predictor< 1 %
- Data Generator< 1 %
Excel Reader (XLS)< 1 %- Statistics< 1 %
- Parameter Optimization Loop Start< 1 %
- Scatter Plot (JavaScript)< 1 %
- File Reader< 1 %
CSV Reader< 1 %Linear Correlation< 1 %Row Splitter (deprecated)< 1 %Row Filter (deprecated)< 1 %Table Column to Variable< 1 %Interval Loop Start< 1 %- PMML Simple Regression Tree Predictor< 1 %
- Date&Time Part Extractor< 1 %
- Shuffle< 1 %
Scorer< 1 %- Tree Ensemble Learner (Regression)< 1 %
- Reference Row Filter< 1 %
- Missing Value< 1 %
- String to Number< 1 %
- Column Aggregator< 1 %
- DB Reader< 1 %
- Table View (JavaScript)< 1 %
- Column Appender< 1 %
Database Reader< 1 %- Java Snippet< 1 %
- MultiLayerPerceptron Predictor< 1 %
- Simple Regression Tree Predictor< 1 %
- Concatenate< 1 %
Column Rename< 1 %- Rule Engine< 1 %
XLS Reader< 1 %- Table Row to Variable< 1 %
- Joiner< 1 %
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Joiner< 1 %- Color Manager< 1 %
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Extract Date&Time Fields< 1 %Column Appender< 1 %- Linear Correlation< 1 %
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- Scorer< 1 %
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Table Row to Variable< 1 %- Chunk Loop Start< 1 %
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Naive Bayes Predictor< 1 %- PNN Predictor< 1 %
PCA Apply< 1 %PCA< 1 %- SMOTE< 1 %
- Random Forest Learner (Regression)< 1 %
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- One to Many< 1 %
- Correlation Filter< 1 %
- Domain Calculator< 1 %
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- Nominal Value Row Filter< 1 %
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Number To String (PMML)< 1 %- Math Formula< 1 %
- RDKit Descriptor Calculation< 1 %
- Fingerprints Expander< 1 %
- Feature Selection Filter< 1 %
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- Numeric Scorer< 1 %
- Number to String< 1 %
- Normalizer (PMML)< 1 %
- Numeric Outliers< 1 %
- Decision Tree View (JavaScript)< 1 %
- Parameter Optimization Loop End< 1 %
File Reader< 1 %Regression Predictor< 1 %Cell Replacer< 1 %- Missing Value Column Filter< 1 %
- Decision Tree to Ruleset< 1 %
Color Appender< 1 %- JPMML Cluster Assigner< 1 %
- Try (Data Ports)< 1 %
- Polynomial Regression Learner< 1 %
- PCA< 1 %
- String Manipulation< 1 %
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- Boosting Learner Loop Start< 1 %
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Loop End< 1 %- Column Resorter< 1 %
- Value Counter< 1 %
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- Box Plot (legacy)< 1 %
- Gradient Boosted Trees Predictor (Regression)< 1 %
- Number Rounder< 1 %
Excel Reader (XLS)< 1 %- String to Date&Time< 1 %
- ROC Curve (JavaScript)< 1 %
Tree Ensemble Predictor< 1 %- Auto-Binner< 1 %
- HiLite Row Splitter< 1 %
- RowID< 1 %
Linear Discriminant Analysis< 1 %- Boosting Predictor Loop End< 1 %
PMML Predictor< 1 %- Category to Class< 1 %
- Replacer< 1 %
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- Decision Tree Learner< 1 %
- PCA Inversion< 1 %
- Random Forest Predictor (Regression)< 1 %
- Low Variance Filter< 1 %
String To Number (PMML)< 1 %- IF Switch< 1 %
- Lift Chart (legacy)< 1 %
- Math Formula (Multi Column)< 1 %
- Date&Time to String< 1 %
- Strings to Binary Objects< 1 %
Parameterized Database Query< 1 %- Java Edit Variable< 1 %
Counting Loop Start< 1 %- k-Means< 1 %
Tree Ensemble Learner< 1 %- Numeric Binner< 1 %
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- Double to Integer< 1 %
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SAS7BDAT Reader< 1 %- Java Edit Variable (simple)< 1 %
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Single Selection Input QuickForm (legacy)< 1 %Value Selection QuickForm (legacy)< 1 %- Date&Time Difference< 1 %
- Date&Time-based Row Filter< 1 %
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- String to Date/Time (legacy)< 1 %
- Outlier Removal< 1 %
Backward Feature Elimination Start (1:1)< 1 %- Feature Selection Loop Start (2:2)< 1 %
Naive Bayes Learner< 1 %- PNN Learner (DDA)< 1 %
- Hierarchical Clustering< 1 %
- Hierarchical Cluster Assigner (local)< 1 %
PCA Compute< 1 %- Create Bit Vector< 1 %
- String to SVG< 1 %
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- Filter Apply Row Splitter< 1 %
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- Reference Row Splitter< 1 %
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- Single sample t-test< 1 %
- Linear Discriminant Analysis< 1 %
- Counter Generation< 1 %
- Random Number Assigner< 1 %
- One Row to Many< 1 %
- Math Formula (Variable)< 1 %
Read Excel Sheet Names (XLS)< 1 %- Weka Predictor (3.7)< 1 %
- Column Filter Configuration< 1 %
- R Snippet< 1 %
- Test Data Generator< 1 %
- Legacy Date&Time to Date&Time< 1 %
- Window Loop Start< 1 %
- Moving Average< 1 %
- Fingerprints< 1 %
- Sum Formula< 1 %
- Table Row to Variable Loop Start< 1 %
- Logistic Regression Learner< 1 %
- Tree Ensemble Learner< 1 %
- Random Forest Learner< 1 %
- Cross Joiner< 1 %
- Duplicate Row Filter< 1 %
- Table Manipulator< 1 %
- Number to String (PMML)< 1 %
- Column Splitter< 1 %
String Replace (Dictionary)< 1 %- Rule Engine Variable< 1 %
- Rank Correlation< 1 %
- Data Explorer< 1 %
- Numeric Outliers (Apply)< 1 %
- SDF Extractor< 1 %
- Stresser< 1 %
- Value Filter (legacy)< 1 %
- Parameter Optimization Loop Start (Table)< 1 %
Views
- Regression Tree View
- Regression Tree View
Workflows
[ISPRENL S11] Group 5 MCO2 WorkflowKNIME Hub- 01_Learning_a_Simple_Regression_TreeKNIME Hub
- 01_Learning_a_Simple_Regression_TreeKNIME Hub
- 01_Learning_a_Simple_Regression_TreeKNIME Hub
- 02_AutoML_Regression_and_Classification_ExamplesKNIME Hub
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